magic quadrant for business intelligence and analytics platforms - january

12.02.13, 12:14Magic Quadrant for Data Warehouse Database Management Systems

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Magic Quadrant for Data WarehouseDatabase Management Systems31 January 2013 ID:G00238144

Analyst(s): Mark A. Beyer, Donald Feinberg, Roxane Edjlali, Merv Adrian

VIEW SUMMARY

The data warehouse DBMS market is transforming due to the rise of "big data" and logical datawarehouses. Unexpectedly, many organizations entered the data warehouse market in 2012 for thefirst time, increasing demand for professional services and causing important changes in vendors'positions.

Market Definition/DescriptionThis document was revised on 6 February 2013. The document you are viewing is the correctedversion. For more information, see the Corrections page on gartner.com.

The market considered in this Magic Quadrant is specifically that for relational database managementsystems (DBMSs) used as platforms for data warehouses (see Note 1). It is important to note that aDBMS does not in itself constitute a data warehouse — rather, a data warehouse can be deployed ona DBMS platform.

A data warehouse solution architecture often uses many different data constructs and repositories.The market now considers broader technology solutions for information management platforms. Theintersection of analytics, cloud and big data forms a new impetus to expand data warehousearchitectures and seek alternative strategies in addition to the relational DBMS at the datawarehouse's core. For example, cloud infrastructure as a service (IaaS) and platform as a service(PaaS) providers that offer technology to transport, manage, analyze and store data may elect to useNoSQL solutions in combination with a variety of other data management technologies. Rumors of thedemise of the data warehouse are misguided, however — a data warehouse is simply a warehouse ofdata, not a specific class or type of technology.

For this Magic Quadrant, we define a DBMS as a complete software system that supports andmanages a database or databases in some form of storage medium (which can include hard-diskdrives, flash memory, solid-state drives or even RAM). Data warehouse DBMSs are systems that, inaddition to supporting the relational data model, are extended to support new structures and datatypes, such as XML, text, audio, image and video content, and access to externally managed filesystems. They must support data availability to independent front-end application software, includemechanisms to isolate workload requirements (see Note 2) and control various parameters of end-user access within managed instances of the data. A data warehouse DBMS is now expected tocoordinate virtualization strategies, and distributed file and/or processing approaches, to addresschanges in data management and access requirements.

This Magic Quadrant covers only the DBMS supplier portion of the overall data warehouse market.Vendors in the data warehouse DBMS market supply DBMS products for the database infrastructureof a data warehouse and the required operational management controls.

Defining the size of a warehouse is less important in 2013, as the ability to access information assets,regardless of location, has become more important. It remains important, however, to distinguish theactual data volume in a data warehouse from the total database size because data volumes undermanagement are one driver toward the logical data warehouse. In the data warehouse market, themanagement of databases often becomes more complex as data warehouse size increases (see Note3). The true measurement of data warehouse platforms will become a combination of volumemanagement and performance optimization for distributed data assets.

For the purposes of this analysis, we treat all of a vendor's products as a set. If a vendor marketsmore than one DBMS product that can be used as a data warehouse DBMS, we note in the sectionspecific to that vendor, but we evaluate all of that vendor's products together as a single entity. Thisis especially important because the influence of the logical data warehouse has created a situation inwhich multiple repository strategies are now expected, even from a single vendor. Strengths andCautions relating to a specific offering or offerings are also noted in the individual vendor sections. Itmay become important in the future to evaluate separate vendor offerings as the logical datawarehouse becomes more pervasive and implementers more frequently pursue best-of-breedstrategies.

To be included in this document's analysis, a DBMS product must have been part of the vendor's

NOTE 1GARTNER'S DEFINITION OF A DATAWAREHOUSE

A data warehouse is a collection of data in which twoor more disparate data sources can be broughttogether in an integrated, time-variant informationmanagement strategy. Its logical design includes theflexibility to introduce additional disparate data withoutsignificant modification of any existing entity's design.

A data warehouse can be much larger than the volumeof data stored in the DBMS, especially in cases ofdistributed data management. Gartner clients reportthat 100TB warehouses often hold less than 30terabytes of actual data (SSED).

NOTE 2EXPECTED WORKLOADS

For the purposes of this evaluation, the workloads weexpect to be managed by a data warehouse includebatch/bulk loading, structured query support forreporting, views/cubes/dimensional-modelmaintenance to support OLAP, real-time or continuousdata loading, data mining, significant numbers ofconcurrent access instances (primarily to supportapplication-based queries at the rate of hundreds if notthousands per minute) and management of externallydistributed processes.

NOTE 3 DATA WAREHOUSE DATA VOLUMES

The data warehouse volume managed in the DBMS canbe of any size. For the purpose of measuring the sizeof a data warehouse database, we define data volumeas SSED, excluding all data-warehouse-design-specificstructures (such as indexes, cubes, stars and summarytables). SSED is the actual row/byte count of dataextracted from all sources. The sizing definitions oftraditional warehouses are:

Small data warehouse: less than 5TBMidsize data warehouse: 5TB to 40TBLarge data warehouse: more than 40TB

NOTE 4 GARTNER'S DEFINITION OF A DATAWAREHOUSE APPLIANCE

A data warehouse appliance consists of a DBMSmounted on specified server hardware with an includedstorage subsystem specifically configured for analyticsuse case performance characteristics. In addition, asingle point of contact for support of the appliance isavailable from the vendor, and the pricing for theappliance does not include separate prices for thehardware, storage and DBMS components.

NOTE 5RESEARCH METHODOLOGY FOR THIS UPDATE

Gartner uses multiple inputs to establish the positionsand scoring of vendors in our Magic Quadrants. Theseare adjusted to account for maturity in a given market,market size and other factors. For this update of theMagic Quadrant, the following sources of informationwere used:

http://www.gartner.com/technology/about/policies/current_corrections.jsp

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product set for the majority of the calendar year in question — in this case, 2012. If a product hasbeen acquired from another vendor, that acquisition must have occurred before 30 June 2012. Anyvendor or product acquired mid-year will be represented by a separate dot until publication of thefollowing year's Magic Quadrant.

There are many different delivery models, such as stand-alone DBMS software, certifiedconfigurations, cloud (public and private) offerings and data warehouse appliances (see Note 4).These are also evaluated together in the analysis of each vendor.

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Magic Quadrant

Figure 1. Magic Quadrant for Data Warehouse Database Management Systems

Source: Gartner (January 2013)

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Vendor Strengths and Cautions

1010data1010data (www.1010data.com) was established 13 years ago as a managed service data warehouseprovider with an integrated DBMS and business intelligence (BI) solution primarily for the financialsector. More recently, it has branched into the retail/consumer packaged goods (CPG), telecom,government and healthcare sectors. 1010data offers the current release of its offering, version 6(released in November 2011), as a managed service, a licensed database and a certifiedconfiguration. At the time of writing, over 250 customers use 1010data's database or solution.

Strengths

Increasingly diverse customer base. In prior years, 1010data has shown significantcapabilities in supporting securities, financial and banking analytic needs. Throughout late 2011and into 2012, 1010data also added customers in the retail and consumer packaged goodssectors, and achieved similarly high levels of satisfaction. This demonstrates that the DBMS'sstrong ability to execute in one market now translates well into other markets. It also indicatesimproved vision overall.

Seamless version migration. Customers find it relatively simple to get 1010data's DBMS upand running. They often report that moving their analytics from other platforms to 1010data's isstraightforward. Similarly, they report that version upgrades are seamless. This is important, as1010data releases new functionality approximately every other week. Since 1010data provides asoftware as a service (SaaS) solution to many of its customers, this should be the case — if itwere not, 1010data's business model would be in trouble. Whether customers operate1010data's offerings themselves or buy them as SaaS, they report no additional licensing costsother than for regular maintenance and support for upgrades. Additionally, 1010data's approachincludes a full platform offering, from data ingest and integration to analytics, and even supportsparticipation in data consortiums.

Load and query performance. Most 1010data customers report that the majority of their datais loaded on a daily basis or even more frequently. Most reference customers report that queryperformance is exceptional, with comments ranging from appreciation of the ability to runcomplex queries on large datasets to the ease with which end users can access the analytic toolsto answer ad hoc queries.

Support and maintenance. Customers report timely, thorough and intimate product support,from knowledgeable professional staff. For the first time, however, we have heard about issues

Original Gartner published research, often utilizingour market share forecasts to establish the breadthand size of a market.Publicly available data, such as earningsstatements, partnership announcements, productannouncements and published customer cases.Gartner inquiry data collected by the authors andthe wider analyst community within Gartner:inputs such as use cases, issues encountered,license and support pricing, and implementationplans.RFI surveys issued to the vendors, in which theywere asked to provide specifics about versions,release dates, customer counts, distribution ofcustomers worldwide and other data points.Vendors could refuse to provide any information inthis survey, at their discretion.Customer reference surveys. Vendors were askedto identify a minimum number of references.Gartner augmented the vendor-providedpopulation by adding Gartner inquiry clientcontacts as potential respondents. Responses werevoluntary, for all participants. These surveysincluded questions to validate customers as currentlicense holders. Additionally (especially in the caseof open-source utilization), customers providedinformation that validated the size and scope oftheir implementations. In addition, customers wereasked to provide information about issues andsoftware bugs, overall and specific sentimentsabout their experience of the vendor, the use ofother software tools in the environment, the typesof data involved, and the rate of data refresh orload. They were also asked about their deploymentplans.Gartner customer engagements in which weprovide specific support were aggregated andanonymized to add additional perspective to theother, more expansive research approaches.

It is important to note that this is qualitative researchand, as such, forms a cumulative base on which toform the opinions expressed in this Magic Quadrant.

EVALUATION CRITERIA DEFINITIONS

Ability to ExecuteProduct/Service: Core goods and services offered bythe vendor that compete in/serve the defined market.This includes current product/service capabilities,quality, feature sets, skills, etc., whether offerednatively or through OEM agreements/partnerships asdefined in the market definition and detailed in thesubcriteria.

Overall Viability (Business Unit, Financial,Strategy, Organization): Viability includes anassessment of the overall organization's financialhealth, the financial and practical success of thebusiness unit, and the likelihood of the individualbusiness unit to continue investing in the product, tocontinue offering the product and to advance the stateof the art within the organization's portfolio ofproducts.

Sales Execution/Pricing: The vendor's capabilities inall pre-sales activities and the structure that supportsthem. This includes deal management, pricing andnegotiation, pre-sales support and the overalleffectiveness of the sales channel.

Market Responsiveness and Track Record: Abilityto respond, change direction, be flexible and achievecompetitive success as opportunities develop,competitors act, customer needs evolve and marketdynamics change. This criterion also considers thevendor's history of responsiveness.

Marketing Execution: The clarity, quality, creativityand efficacy of programs designed to deliver theorganization's message in order to influence themarket, promote the brand and business, increaseawareness of the products, and establish a positiveidentification with the product/brand and organizationin the minds of buyers. This "mind share" can bedriven by a combination of publicity, promotional,thought leadership, word-of-mouth and sales activities.

Customer Experience: Relationships, products andservices/programs that enable clients to be successfulwith the products evaluated. Specifically, this includesthe ways customers receive technical support oraccount support. This can also include ancillary tools,customer support programs (and the quality thereof),availability of user groups, service-level agreements,

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http://www.1010data.com/



of scaling and that 1010data's staffing needs to keep pace with 1010data's growth.

Cautions

North America-centric. 1010data's growth and customers remain heavily concentrated inNorth America — it has almost no demonstrated customers elsewhere. This, however, isunderstandable given its present size and reach. 1010data's clients are predominantly very largeorganizations, with most of its reference customers reporting annual revenue of over $1 billion.

Silo access. Many customers report that although 1010data's solution delivers superiorperformance, access to the data and the ability to use the engine from other front-end analytictools is less than satisfactory. In cases where other analytic tools need to access the data,1010data is restricted by its interface and, in some respects, by a lack of transparency aboutperformance statistics and performance-tuning capability.

Vulnerability to "standard" vendors. Reference customers report that 1010data's solution isused in two or more departments as a key analytics platform, but that it is often not theirenterprise standard. This raises the threat of incumbent vendors forcing out 1010data byoffering corporate standards. We consider this a minor risk, however, as customers considerthat 1010data provides an analytic support capability that is unique to their use case — and thatit challenges the supposed need for a "standard." Alternative "standard" vendors generallyinclude all the Leaders in this Magic Quadrant. Stated reasons for selecting other vendors rangefrom high pricing by 1010data to, in a few cases, better performance by the customer'sstandard vendor.

A different solution approach. 1010data is something of a maverick innovator in the field ofanalytics, but this does not make it immune to effects of major market forces. Many end usersare demanding the components of a logical data warehouse, which 1010data is able to introducebehind the scenes and transparently — but it is a different matter if customers want to licensethese components from 1010data and run a logical data warehouse themselves.

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ActianActian (www.actian.com) (formerly Ingres) offers two products: the commercially licensed Vectorwiseaimed at analytic data warehouses (version 2.5 was introduced in June 2012) and the general-purpose open-source Ingres DBMS (version 10 was introduced in May 2012). Actian claims over 65customers for Vectorwise. Ingres, one of the original relational DBMS (RDBMS) engines, has a 30-year history and over 14,000 customers running mission-critical applications, including datawarehouses.

Strengths

Increasing mind share. Actian rebounded significantly in 2012 from the disruption itexperienced the previous year as it reorganized and introduced new products. It introduced newDBMS versions, added experienced executives, and expanded and increased the leverage of itspartner network. Actian is now delivering logical models with independent software vendors andsystem integrators in several vertical markets.

Focus on performance. Vectorwise remains Actian's focus for analytic use cases, and itsexcellent performance dominated Gartner's customer interviews and surveys. Vectorwiseexploits hardware assists, turning columns into vectors and processing them in x86 chipregisters to capitalize on instruction parallelism and on-chip caching. This can have dramaticimpact on performance, as noted by one customer interviewed for this research who wasreferred to Vectorwise by Intel. Vectorwise delivered leading 1TB nonclustered TPC Benchmark H(TPC-H) results in 2012, but this should be viewed in perspective as this is a highly specificsector of the market. As hardware prices drop, Vectorwise's price/performance continues tomake it very attractive for smaller projects — most of the interviews and surveys Gartnerconducted concerned those at the low end of the size spectrum.

Continued improvements to functionality and delivery options. In 2012, Actian addedmore analytic SQL functionality to Vectorwise and revealed a road map for additionalcapabilities, including online analytical processing (OLAP) features and a Hadoop connector.Actian also introduced Vectorwise appliances with Lenovo and Huawei, certified configurationswith Dell, HP and Cisco, a managed service option with remote monitoring, and a cloud offeringwith Rackspace. Ingres contains most of the features necessary for data warehousing, such aspartitioning, compression, parallel query, multidimensional structures and a community-drivengeospatial offering.

Aggressive pricing to gain new customers. Vectorwise has continued to win customers fornew installations and furthered its long tradition of having loyal supporters. Vectorwise's core- ordata-based licensing model has proved attractive, and several customers mentioned price as akey feature. Some Actian customers mentioned data warehouse and analytics deployments inwhich technologies from open-source partners Pentaho and Yellowfin are combined withVectorwise to lower the cost even further.

Cautions

Lack of functionality. Vectorwise is key to Actian's ability to support analytic data marts, butthe company must continue to enhance its data warehouse, storage management and mixedworkload management capabilities if it is to compete with larger, more mature vendors andmeet the needs of the broader data warehouse DBMS market. Vectorwise has gained moreanalytic SQL constructs, but still needs stored procedures, user-defined functions and datatypes, table partitioning and other features to close the gap with competitors. Actian's road mapto add these capabilities looks aggressive and will require a strong focus on execution. The lack

etc.

Operations: The ability of the organization to meet itsgoals and commitments. Factors include the quality ofthe organizational structure including skills,experiences, programs, systems and other vehiclesthat enable the organization to operate effectively andefficiently on an ongoing basis.

Completeness of VisionMarket Understanding: Ability of the vendor tounderstand buyers' wants and needs and to translatethose into products and services. Vendors that showthe highest degree of vision listen and understandbuyers' wants and needs, and can shape or enhancethose with their added vision.

Marketing Strategy: A clear, differentiated set ofmessages consistently communicated throughout theorganization and externalized through the website,advertising, customer programs and positioningstatements.

Sales Strategy: The strategy for selling product thatuses the appropriate network of direct and indirectsales, marketing, service and communication affiliatesthat extend the scope and depth of market reach,skills, expertise, technologies, services and thecustomer base.

Offering (Product) Strategy: The vendor's approachto product development and delivery that emphasizesdifferentiation, functionality, methodology and featureset as they map to current and future requirements.

Business Model: The soundness and logic of thevendor's underlying business proposition.

Vertical/Industry Strategy: The vendor's strategyto direct resources, skills and offerings to meet thespecific needs of individual market segments, includingverticals.

Innovation: Direct, related, complementary andsynergistic layouts of resources, expertise or capital forinvestment, consolidation, defensive or pre-emptivepurposes.

Geographic Strategy: The vendor's strategy to directresources, skills and offerings to meet the specificneeds of geographies outside the "home" or nativegeography, either directly or through partners,channels and subsidiaries as appropriate for thatgeography and market.


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of some of these capabilities causes problems when deploying logical data warehouses.

Good support for "bugs" not enough. The maturity of Vectorwise presents a challenge.Several customers noted early challenges with bugs, though they all praised Actian for its rapidresponse in addressing them. Still, "fixing bugs for customers" is not a scalable product supportmodel — Actian needs to put more emphasis on product quality.

Market perception recovery under way. Although Actian has begun to raise awareness of itsnew brand name and portfolio expansions, it has yet to regain much of its former markettraction. Its aggressive new vision must be matched by strong marketing and sales execution.With over 65 customers to call on as references to help sell its innovative product, Actian canand must continue to change the market's perception. It must accelerate its revenue andnumbers of new customers more quickly than it has so far.

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CalpontCalpont (www.calpont.com), a private corporation based in Frisco, Texas, launched InfiniDB, ananalytic columnar DBMS platform with parallel query performance, in February 2010. It currently hasabout 50 named customers. This is a software solution — Calpont does not offer appliances, but itdoes work with partners offering prebuilt solutions. The latest release of InfiniDB is version 3.5, whichbecame generally available in 2012.

Strengths

Targeted regions and markets. Regional-specific vendors often do not fare well — and wecontinue to view a lack of global presence as a drawback — but Calpont does a good job offocusing its delivery resources on North America, and it does also have a small but significantpractice in Germany representing EMEA. Calpont addresses vertical markets differently frommany vendors (especially through its partnership network), with more reference customers inthe IT services, wholesale, agriculture and online media sectors than in the traditional fields oftelecommunications, financial services/banking and retail. Overall, this constitutes a Strength asit demonstrates focus and an ability to identify specific use cases suited to the functionality of itsproduct. From a competitive perspective, Calpont most frequently finds itself up against HP(Vertica), EMC (Greenplum) and Oracle (Exadata), while keeping its resources focused on theanalytics data mart sector.

Technology partners and big data. Being a small company gives Calpont some opportunity totry new ideas. InfiniDB uses a "MySQL front-end" to attract the huge number of MySQL usersworldwide. For big data, Calpont has adopted a MapReduce parallel programming paradigm tosupport parallelism across multiple servers for performance and scalability. Its road mapincludes many new features and enhancements for performance, stability, scalability and highavailability, and the beginnings of support for a logical data warehouse with connectors toHadoop.

Multiple pricing options. Of the vendors in this Magic Quadrant, Calpont has one of the moreflexible approaches to pricing, with three options: (1) the free InfiniDB Community Edition forsingle-server deployments; (2) cloud pricing per terabyte; (3) on-premises deployments pricedby terabyte or number of processor cores. This enables customers to choose the bestdeployment option for them in light of their location and the amount of data.

Ease of deployment and use. Our survey of reference customers asked them to answer avariety of positive sentiment and negative sentiment questions. Calpont scored low for negativesentiment (a good result) and came in the top third for positive sentiment. By far the mostcommon observation made by customers was that Calpont's offering could be "dropped in"quickly to replace MySQL — with some reporting that no changes were required to any of theiranalytics, and others that they now use the same GUI tools for InfiniDB as they did for MySQL.

Above-average value. In customer ratings, Calpont scored well above average in terms ofvalue for price paid, although Calpont's customers generally were not on the current version anddid not plan to become current in the next nine months. Calpont states that its 3.0 version wasdeployed primarily to enhance the solution's scaling and that customers on version 2.x thereforehad no immediate reason to upgrade. Reference customers report high satisfaction regardless ofthe version they are using, which seems to support Calpont's assertion that version upgradesare driven more by demands for the new functionality.

Cautions

Technical challenges, functional weaknesses. Customers report a lack of some basic SQLand analytical functions (first/last, variance, ranking). As with most columnar DBMSs, insertoperations are reported to be slow, but Calpont's bulk loader (CPImport) compensates for this.

Complex query issues. Users reported that even a small number of users issuing contendingcomplex queries can cause the DBMS to stop functioning properly, even to the point of requiringa restart. Some of these problems appear to emerge from JOIN issues. Calpont says theseissues are addressed in newer versions, but, as noted above, many customers have yet to moveto these versions.

Lack of high availability and disaster recovery (HA/DR). According to referencecustomers, Calpont lacks traditional, robust HA/DR operations, incremental backup and restorebeing identified as a specific area of weakness. Calpont offers HA with its multiserverconfiguration, but most customers have not purchased this configuration (hence theircomments, probably). After considering the technical challenges, functional weaknesses,complex query issues and lack of robust HA/DR, Gartner takes the view that Calpont is notready to manage or drive a logical data warehouse, although InfiniDB could play a part in one.

Continued struggle to find vision. Calpont has been in business for over 12 years, but did


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not release InfiniDB until 2010. Its customer base remains small, and it has almost no presencein Gartner's customer inquiry base. End users should consider Calpont a "new" vendor whenevaluating its products.

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EMCGreenplum (www.greenplum.com), part of EMC's Data Products division, offers a massively parallelprocessing (MPP) data warehouse DBMS that runs on Linux and Unix. Version 4.2 of Greenplum'sdatabase was released in November 2011. EMC does not publish a count of named customers butGartner estimates there are over 400, based on our existing information and analysis of calls to ourclient inquiry service. EMC offers the Greenplum database for license, as an appliance and as acertified configuration model (it cooperates with partners to deliver certified configurations). EMC(Greenplum) was one of the first vendors to combine DBMS processing with distributed processingclusters (Hadoop).

Strengths

Increasingly global presence. EMC has data warehouse customers in Australia, Canada,China, Indonesia, Japan, Malaysia, South Korea and the United States. It offers Greenplumcustomers a worldwide sales and support organization, and the product's reach and presenceare expanding rapidly worldwide. That EMC's Ability to Execute is lower on this year's MagicQuadrant reflects the company's promising return to a focus on visionary leadership.Nevertheless, following EMC's recent repositioning for cloud offerings and distributedinfrastructure, it will take more evidence of customer adoption to recover in the executionratings.

Broad appeal to vertical markets. To complement its global diversity, EMC has a broad baseof customers in vertical markets: telecommunications, financial services, oil and gas, utilities,software and IT, among others. EMC's announcements claim multiple customers with annualrevenue of over $1 billion, though most of the reference customers it identified for our MagicQuadrant survey reported annual revenues of $1 billion or less. Additionally, there were fewernew customers among EMC's references this year — but the existing ones increased theirinvestments in its solutions. The same mixed picture emerges from Gartner's inquiry data, so wecan make no conclusion about EMC's customer growth in 2012.

Reliability and performance. Customers report that EMC's system is self-healing in the eventof disk or server failures. A few of the reference customers volunteered, without prompting, theinformation that any failures could be attributed directly to hardware faults and were not thefault of the DBMS. There were instances of advice from EMC customers in the customerexperience section of our survey that recommended the use of resource queuing functions toovercome some database performance issues. Customers reported a wide range of datawarehouse/database sizes, from 1TB to over 300TB, and at least one customer reported that itwas approaching 2PB. Interestingly, the largest number of connected users was in the 100TBrange and a small number of users in the very large size range — the latter indicates strategicuse by experienced and intensive analysts in very large warehouses. Gartner expects thecombination of EMC's stable market delivery and the expertise of former Greenplum staff indesigning and developing software to produce a solid recovery by 2014.

Winning value proposition. Even customers who reported that EMC's pricing is high wereimpressed by the value proposition. References added that there are very few separately pricedand licensed add-ons to the DBMS. One reason for this winning combination is the Greenplumdatabase's roots in managing and analyzing big data assets — EMC's reference customersreported the highest rate of production of any of the vendors in this Magic Quadrant for theimplementation of NoSQL, MapReduce and other big data technologies managed or adjacent toGreenplum data warehouses. In addition, EMC claims rapid adoption of Greenplum Chorus, aplatform that supports self-service provisioning of the data warehouse architecture "sandboxes"that analysts use when exploring data in the warehouse.

Cautions

Reportedly inconsistent HA/DR. EMC's reference customers were less appreciative of itsresource queue management in relation to the overall operation of its DBMS — severalcommented that this made setting up HA an issue. Some also reported problems due to theabsence of incremental backup capability, with large databases specifically affected. EMC'sresource management also attracted criticism, with some noting that hardware balancing is theuser's job and not managed well by the DBMS. In November 2012, EMC acquired Israelicompany More IT Resources with a view to using its flagship MoreVRP product to improve itsperformance monitoring, historical playback of issues and real-time resource allocation. Thisindicates that EMC is fully aware of the situation and determined to address it. EMC hasincremental backup capabilities on its road map for 2013, which is also likely to include user-configured data retention policies.

Compatibility and loading issues. Customers report that loading issues exist in relation todata type compatibility and extraction, transformation and loading tool compatibility.

EMC's support for its DBMS. Greenplum customers reported some difficulty in moving to EMCas the supporting vendor, due to a loss of intimacy and a lack of product understanding,especially from low-level support staff. Recent indications are that EMC's support model isimproving, but, given the additional HA/DR, compatibility and loading issues, this remains afactor that prospective clients should treat with low level but insistent concern. It is here thatthe real challenge of combining the capabilities of EMC and Greenplum lies.

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http://www.greenplum.com/




ExasolExasol (www.exasol.com) is a small DBMS vendor based in Nuremberg, Germany. It has been inbusiness since 2000. Its first in-memory column-store DBMS, EXASolution, became available in 2004.EXASolution is still used primarily as a data mart for analytic applications, but also occasionally insupport of logical data warehouses. Exasol reports 38 customers in production and expects to have50 customers by January 2013.

Strengths

Support for the logical data warehouse. Clients have always praised Exasol's in-memoryDBMS for its speed of query processing. However, Exasol's hybrid model, which manages databoth in memory and on disk, is not always sufficient to support the diversity of use cases arisingfrom the need to manage data large volumes of varied data at high speed. Exasol hasrecognized this shortcoming and, with the release of EXAPowerlytics, has been adding supportfor external functions such as MapReduce and support of the R statistical programminglanguage. Exasol's support for the logical data warehouse will be further extended by theaddition of database link capabilities in the next release.

Customer adoption. Exasol won new customers throughout 2012 as it benefited from themarket hype about in-memory computing. This hype, together with Exasol's effective resellingstrategy in Europe and Israel, proved a winning combination.

Growing maturity of product offerings. As Exasol attracts more customers, it is alsomaturing its products. EXAStorage, currently in beta testing and available as a controlledrelease, is a complementary storage layer that manages data persistence to disk in order to addsupport for more redundancy, failover and disaster recovery. It is available as a stand-alonestorage solution in addition to EXASolution.

Cautions

Availability of skills and support for third-party tools. Like other small vendors, Exasolsuffers from the limited availability of skills in this market and limited support for third-partytools, such as BI, data integration and database management tools from Dell (which acquiredQuest Software in September 2012). However, Exasol offers standard SQL connectors and APIsthat enable interoperability with the offerings of major vendors.

Completeness of offering. Respondents to our customer survey indicated that completenessof offering remains an issue for Exasol, specifically in relation to auditing, monitoring andadministration capabilities. Some also mentioned that upgrading to newer versions could be achallenge. Even so, the vast majority of Exasol customers surveyed indicated that they plannedto purchase additional software licenses from the company in the next 12 months.

Increased competition. Although in-memory DBMSs are benefiting from the hype surroundingSAP Hana, the number of offerings entering the market is also growing as other small vendorsemerge. This means Exasol must improve its marketing and communications in order todifferentiate itself from its competitors.

Viability. As for many small vendors, customers reported concerns about the long-term viabilityof Exasol's technology and the risk associated with this vendor's status as a potential acquisitiontarget. But Exasol's plans to open new offices in the U.K. and Brazil, and its growing customeradoption, are reassuring signs.

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HPHP's (www.hp.com) portfolio of platforms for data warehousing is anchored by Vertica, a column-store analytic DBMS it acquired in May 2011. Vertica is delivered as software for standard platforms(excluding Windows), and as a Community Edition (free for up to 1TB of data, three nodes). Alsoavailable are the HP Vertica Analytics System appliance, cloud versions (via HP Cloud Services andAmazon Machine Images), and Datawarehouse On Demand, a hosted, managed service. HP FactoryExpress (in predefined certified configurations based on its ProLiant DL380 servers) are available forVertica and Cloudera Distribution including Apache Hadoop (CDH). HP now claims over 2,250 direct,OEM and channel customers for Vertica, an increase from the 500 customers Gartner reported in the2012 edition of this Magic Quadrant, due primarily to the inclusion of OEM and channel customerspreviously excluded from this count.

Strengths

Price/performance value. Vertica customers we interviewed identified performance,compression and ease of deployment as key advantages; ease of setup and automated databasedesign were mentioned frequently (one respondent's organization, located in a country withouttechnical support personnel at the time of deployment, set up its system entirely unaided).Vertica's pricing model, which is based on the amount of source-system-extracted data (SSED)loaded into the DBMS, rather than the number of users, servers, chips or processor cores, alsoremains in favor. We expect that the value customers attribute to the solution will be asignificant benefit to HP as it tries to recover in terms of overall execution.

Analytics use case optimization. Vertica was an early leader in analytic platforms. Itdesigned for scalable shared-nothing deployment on commodity MPP clusters with built-in HA,active replicas and automated node recovery, and its separation of an in-memory write-optimized row store from a read-optimized disk-based column store improved load speed (atraditional problem for columnar databases), pointing the way to more general use of in-memory database technology. The Vertica team delivered on its planned road map with therelease of version 6 in June 2012. It has doubled the size of its team since acquisition, rampedup its professional services effectively, and extended its vision based on emerging projects with

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HP Labs' substantial R&D resources.

Logical data warehouse ("getting to the unstructured stuff"). Adding to Vertica's datacompression, multitier storage model and extensibility, version 6's new Flex architecturesupports querying across additional data stores, such as Autonomy, ArcSight and Hadoop. Thisenhances Vertica's flexibility and supplements its in-database execution capabilities, which nowinclude expanding support for R parallelization, pattern matching, statistical and linearregression, geospatial analysis and social graphing. An external tables facility and the ability tomaterialize tables during loading add reach to these features. Version 6 points to the future useof federation across other RDBMSs, as well as detection of external metadata for "late binding,"laying the foundation for key elements of the logical data warehouse vision. Again, while thevision for this solution is present, its execution has yet to be achieved, and we expect this areato be another contributor to HP's recovery on the Ability to Execute axis of the Magic Quadrant.

Platform optimization tools. Vertica has begun to benefit from HP's hardware and systemmanagement resources. The HP Vertica Management Console and HP Cluster ManagementUtility (CMU) provide Web-based management, configuration and monitoring platforms forVertica deployments that include cluster visualization, alerting, performance and diagnosticgraphs, and "push-button" cluster operations. The HP CMU offers real-time 3D visualization ofphysical assets, time-series performance views, and a "single pane of glass" for the entirehardware stack.

Partners and alternative delivery models. Vertica's SaaS and embedded capabilities havebegun to attract the attention of HP partners, including large system integrators like Accentureand Capgemini, as well as smaller vertically focused shops. Independent software vendors arecreating products using Vertica, and HP Enterprise Services now provides data models, filling agap to enhance the data warehouse value proposition. HP is supporting the MyVerticacommunity's participation in open-source code sharing via GitHub.

Cautions

Poor coordination of unstructured and structured data strategy. Throughout 2012, HPlacked comprehensive integration road maps for the multiple products in its portfolio. Theseremain under separate management organizations, but a new relationship between Autonomyand Vertica, which sees peer general managers reporting to the executive vice president of HPSoftware, seems to have stabilized matters. HP needs to capitalize on this to attract customersseeking to incorporate unstructured data, a key driver of the logical data warehouse. AlthoughHP has mitigated some of its organizational problems, its progress toward the logical datawarehouse still lags behind.

Risk to overall market confidence. At the time of writing, HP's acquisition of Autonomy isunder financial review. There is a risk that the adverse publicity associated with the recentmultibillion-dollar write-downs related to the acquisitions of EDS and Autonomy will add to theconcerns Gartner clients have raised about their confidence in HP's ability to manageacquisitions effectively.

Slow to exploit global presence. In 2012, Vertica's customer base was still overwhelminglyNorth American, a reflection of HP's slowness to capitalize on its worldwide sales and marketingreach. Appliance marketing has been underwhelming (few Gartner customers even know that HPappliances exist), while Oracle's Exadata messaging continues to dominate the airwaves. HPneeds to make a substantial effort — including substantial investments — to raise its profile.Inquiries to Gartner about Vertica remain low, reinforcing the impression of a troublingslowdown in Vertica's momentum. However, new sales force training and compensationarrangements seem to be accelerating global adoption.

Need for additional features. High performance alone is no longer a differentiator. Vertica'scompetitive posture could be improved by the addition of HP's system management leadership,but, despite progress, customers continue to point to missing DBMS features, such as storedprocedures. To keep pace, HP must validate its strategic information platform ambitions bycontinuing to improve its workload management, volume credentials, user-defined functions andvision for the logical data warehouse.

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IBMIBM (www.ibm.com) offers stand-alone DBMS solutions, as well as data warehouse appliances,marketed as the IBM Smart Analytics System (ISAS) family and under the Netezza brand. IBM's datawarehouse software, InfoSphere Warehouse, is available on Unix, Linux, Windows and z/OS. IBM alsohas a version of the Netezza product that attaches to System z products as an analytic accelerator —the IBM DB2 Analytics Accelerator (IDAA). In late 2012, IBM released the PureData System line ofDBMS appliances, in the process rebranding Netezza as the PureData System for Analytics, thePower-based ISAS as the PureData System for Operational Analytics, and the System z ISAS withIDAA as the zEnterprise Analytics System. IBM has thousands of DBMS customers worldwide, butdoes not report specific customer and/or license counts. There are more than 1,000 appliancecustomers (Netezza and ISAS combined).

Strengths

Logical data warehouse practices. As early as 2010, IBM began to reposition its datamanagement and integration stack to align it more closely with the (then unnamed) logical datawarehouse concept. In addition, technical collaboration between IBM and some of its partners(notably Composite Software with the Netezza product line) is bringing this concept into theDBMS and appliance spaces. Close coordination between IBM's big data processing andintegration practices and data warehouse offerings forms the third "leg" of its logical datawarehouse proposition, the other two being repositories and virtualization. In 2012, IBM aligned


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its sales, product marketing, product management and product positioning around the Big DataPlatform, its physical architecture for the logical data warehouse. In addition, IBM's GlobalServices organization continues to develop delivery methodology for new data warehousingpractices. We expect that IBM will continue along this trajectory and that in the coming years itwill vie with other Leaders for visionary leadership. When it comes to making sure that differentsolutions and platforms are increasingly integrated both technically and from a support andmanagement perspective, IBM continues to make progress. For example, the System-z-basedmodels of the ISAS family (9700/9710) were updated in 4Q12 to include the IDAA (powered byNetezza technology) and rebranded the zEnterprise Analytics System. This is a significantmilestone in the evolution of System z as a multiworkload platform that includes high-performance analytics.

New PureData System appliances. In 2012 IBM released the PureFlex System, followed bythe PureData System line of DBMS appliances. Notably, the PureData systems — PureDataSystem for Analytics, powered by Netezza technology, PureData System for OperationalAnalytics (DB2 with data partitioning) and PureData System for Transactions (DB2 onlinetransaction processing [OLTP] with pureScale clustering) — are geared to data warehousing andanalytics. The PureData System for Analytics uses the next generation of Netezza hardware andsoftware, while the PureData System for Operational Analytics replaces the Power 7 SmartAnalytics System. Additionally, IBM has delivered a multitemperature storage solution for datawarehousing in DB2 10 that automatically manages flash memory and disk storage for highperformance. Netezza, along with the Power-based Smart Analytics, joined the Pure Systemsfamily under the PureData System brand name in October 2012.

Global presence, sales, delivery and support. With offices or distributors in virtually everyregion of the world, IBM delivers not only software support but also implementation expertiseand services. IBM's customer count includes those with warehouses more than three years oldand those with warehouses less than three months old. IBM reports 24% growth for its Netezzaproducts alone. IBM's reference customers include organizations in the government, healthcare,financial services, energy and utilities, wholesale, oil and gas, and business and consumerservices sectors, as well as many others. With customers ranging from those with less than$100 million in annual revenue to those with over $5 billion, IBM serves both small organizationsand large global organizations. Additionally, the introduction of per-terabyte pricing is helpingIBM gain traction with customers expecting incremental growth from their warehouse. IBMappeared more frequently in the competitive situations discussed by Gartner clients in 2012than in 2011, which indicates a gradual increase in mind share. We expect this to continue.

Extent of use and commitment. IBM often exists side-by-side with competitors, butorganizations that use IBM as their data warehouse DBMS standard become almost exclusiveusers of IBM solutions — these customers typically reach a point at which they exclude other topcompetitors from consideration. This gives IBM an entrenched customer base that seems readyto use even more IBM products. Sizes of databases reported by customers range from 1TB to2PB. For all sizes, loading occurred most frequently on a daily basis, with about 20% ofcustomers reporting loading more frequently than daily.

Enhanced performance, pricing and end-user involvement. The traditional method of datawarehouse and analytics deployment usually involves extended requirements specificationbetween warehouse end users and the IT department that deploys the warehouse. In 2012, IBMintroduced a series of DBMS product enhancements focused on allowing end users to deploytheir own solutions — in effect, empowering end users to do more for themselves. At the sametime, IBM's new features audit these activities and enable IT professionals to monitor the self-service environment. InfoSphere Data Click combines data access management with data exportdesign, enabling users to self-serve their data access. Temporal data, multitemperature storage,adaptive compression and real-time load enhancements (what IBM calls "continual ingest") wereintroduced. Many of these new features and products are aimed at enhancing IBM's Big DataPlatform. Finally, IBM's partner network was active in 2012, with new analytic solutions thatcombine appliance delivery with best practices.

Cautions

Complexity, which demands improved field experience. Customers reported a distinctpreference for IBM to provide direct support for its products in all cases. Issues were reportedconcerning incomplete documentation for advanced features. Additionally, customers reportedthat IBM's partner network demonstrates weakness in supporting products and that up-to-dateexpertise on the latest features is difficult to obtain. During 2012, IBM made major changes toits Partner program, including training, prebriefings on products and closer alignment with theIBM sales force in an effort to improve customer experience in the field. We believe the newPartner training will have a positive effect in future years, but that at present it leaves IBMvulnerable to partners "knowing too much" about the complexity of implementations andtherefore not selecting IBM for their customers.

Need for further improvement in IBM's mind share. With Netezza, Watson and InfoSphereBigInsights (an Apache Hadoop-based offering), IBM has begun to win the mind share it needsto increase its sales in the data warehouse and analytics data management space. Gartnernoted a small increase in the number of mentions of IBM in competitive situations during calls toour inquiry service and bid reviews in 2012, and we will continue to monitor its progress.

No market awareness of attractive pricing and good support. Overall, IBM scored well incustomers' references and in terms of our inquiry service data. But even with high marks fortechnology, implementation and vision/road map, IBM's issues with support and pricing causeconfusion, with reference customers awarding these aspects of IBM's business scores fromexcellent to neutral and, in isolated cases, very low. This range indicates an inconsistency thatIBM must address as it starts to win mind share; otherwise it will fail to benefit from anyimprovements in n the market's momentum. Judging from IBM's references, the company is onan upward trend for both pricing and support — but IBM must focus on making sure the market



knows that these actually are IBM advantages.

Lagging in-memory strategy. IBM has a vast number of products that use in-memorycomputing, including solidDB, but its only in-memory solution for the data warehousing andanalytical market is Informix Warehouse Edition. By contrast, IBM's main competitors alldelivered additional in-memory DBMS capabilities in 2012. IBM must take a leadership positionin in-memory computing if it is to stay competitive with the other vendors.

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InfobrightInfobright (www.infobright.com) is a global company with a steadily increasing customer base for itscolumn-vectored, highly compressed DBMS. Open-source (Infobright Community Edition [ICE]) andcommercial (Infobright Enterprise Edition [IEE]) versions are available, and the company announceda new Infopliance in September 2012. Infobright claims to have 300 customers, a 50% increase overits 2011 numbers.

Strengths

Successful niche focus. Infobright has continued to focus on operational technology data(what it calls "machine-generated data"); its president blogged in 2012 that "We are notdesigned to be an enterprise data warehouse." Nonetheless, Infobright can play a significant rolein the logical data warehouse, managing growing amounts of data from sources such ascustomer data records (in the telco space, where it has had strong success with partners), smartmeter data (in the utilities space) and clickstream data from Internet interactions. Infobright'scontinuing pursuit of OEMs and partnerships with software vendors including Pentaho, Jaspersoftand Talend has proved successful. In 2012 the company began a new initiative with theintroduction of its Infopliance, which is designed to tap the growing interest in appliance formfactors. All of this relates to the rise of big data.

Compression and other performance technology. Infobright's early positioning based onadding a column-store file system to MySQL was successful, but the company has movedbeyond that position, and only two of the Infobright customers interviewed for this MagicQuadrant referred to MySQL. The Knowledge Grid in-memory metadata store gives Infobrightthe ability to enhance performance through selective data decompression in processing queries.This is one reason for the excellent performance cited by customers; another is the superiorcompression technology that makes Infobright such an effective solution with machine-generated data. Delivering both these benefits without conflict is one of the product's strongestdifferentiators.

Price/performance balance. Infobright also features a Distributed Load Processor for parallelloading of data into the system at very high speeds, and was quick to provide connectivity toHadoop Distributed File System (HDFS) data. Use cases for machine-generated data stored inHadoop and extracted into Infobright are prominent in the company's customer base, and theyrepresent a compelling price/performance opportunity.

Simplicity. Customer interviews and surveys highlighted not only the speed and compressionability of Infobright's products, but also their simplicity. The column-store design reducesdependence on special index and aggregate creation, while the Knowledge Grid's contribution tooptimization reduces time requirements for specialist tuning activities. This simplicity willcontinue to attract OEMs, which will base vertically specialized systems on Infobright products.The simplicity of management, the scalability and the compression ability all interest OEMslooking to embed a DBMS that requires little support on their part.

Low pricing. Infobright remains one of the few open-source column-store DBMS vendors, withICE — and, as the market now knows, "open source" does not mean free. Infobright alsogenerates revenue from IEE using a commercial license rather than a General Public License,with a subscription support model based on the amount of SSED stored in the system.Reference customers continue to report low prices and lowered barriers to adoption with thismodel, and Infobright's substantial growth rate shows its strategy to be a success. Infobright is,however, something of an outlier in that its financial and technical success is based on a verygood performance overall in the Niche Players quadrant.

Cautions

Focus that limits market expansion. Infobright has done well with its focus on machine-generated data, and has made it clear that this focus will continue. But both existing andprospective customers need to address other data management use cases. Our interviewsuncovered dissatisfaction with update speed (not a typical requirement for machine data, whichis typically append-only), and with the need for a better HA capability. Again, HA has nottypically been a focus in machine-generated data use cases, but growth in Infobright's customerbase will bring this issue forward. The Infopliance may well prove to be a solution to this issue,but it will take time to validate its effectiveness. Infobright is not partnering for the sales andsupport of this appliance, and this will limit its early visibility and the ability to provide supportfor hardware issues in early deployments.

Absence of management and optimization tools. Prior issues with new releases seem tohave been eliminated, but the lack of management software remains an issue — a typical onefor small vendors. Third-party software vendors are not quick to commit to working with smallsoftware companies, and this puts more pressure on Infobright to develop its own managementsoftware.

Looming MySQL issues. In prior research we have highlighted the risks associated withInfobright's use of MySQL, as MySQL is owned by Oracle. Oracle has continued to enhance thisproduct, but the expiration of Oracle's agreement with the EU is drawing closer, and with it thepossibility that Oracle will seek to change agreements with OEMs. Infobright and its customers


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should remain vigilant.

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KognitioKognitio (www.kognitio.com) started out offering a database management system for datawarehouses as Whitecross in 1992 and a managed service in 1993. It now has customers using theKognitio Analytical Platform either as an appliance, a data warehouse DBMS engine, datawarehousing as a managed service (hosted on hardware located at Kognitio's sites or those of itspartners), or as a data warehouse platform as a service using Amazon Web Services (AWS).Kognitio's total customer count is less than 50.

Strengths

Pricing model. Kognitio has always led with innovative pricing models. It pioneered databaseSaaS offerings of a complete data warehousing service, not just cloud hosting of a datawarehouse. Kognitio also has an "on demand" cloud offering, which is available on AWS. Most ofits new customers are adopting the Kognitio Cloud offering. The company recently announced afree version for small deployments with up to 128GB of data in RAM (persistent storage is freeand unlimited). What makes Kognitio more interesting still is the ability to switch betweenoptions.

Early elements of the logical data warehouse. Kognitio has been progressing toward thelogical data warehouse through Hadoop integration that combines the speed of query processingand analytical capabilities of the Kognitio Analytical Platform with management of large volumesof data hosted on a Hadoop distribution. Kognitio's logical data warehousing strategy is alsosupported by dedicated partnerships with Hortonworks and MetaScale.

Great performance. Respondents to our customer reference survey continued to supportKognitio's performance capabilities. Kognitio offers an in-memory, row-based DBMS that hasbeen on the market for many years and that has consistently satisfied clients with its greatperformance, as it makes it easier to use and run ad hoc queries.

New positioning. In 2012, Kognitio recognized the shifts in the market from traditionalwarehousing to what we call the logical data warehouse. The new features Kognitio released forits product, and their positioning, have extended the product's capability by enabling it tomanage external processes and data.

Cautions

Long-term viability challenged by low market visibility. Despite having a good product,Kognitio grows slowly, typically adding fewer than 10 clients a year. This shows that the vendorfaces a challenge to improve its visibility in the market, despite all the hype about in-memoryDBMSs generated by SAP. Consequently, reference customers had concerns about thecompany's long-term viability. Any small vendor must address visibility issues and balance thisneed with an appropriate focus of resources. However, it should be remembered that Kognitiohas been in business for 25 years and demonstrated financial viability throughout that time.

Integration with third-party tools. Clients reported that interoperability with third-party dataintegration and business intelligence tools remains an issue. This problem is compounded by thechallenges posed by the lack of investment by software integrators in interoperability withKognitio's technology and the limited availability of skills, as is often the case with smallervendors.

Lack of global execution, so far. Kognitio, a U.K.-based company, has been trying to enterthe North American market by hiring U.S. staff and opening offices in New York. Although thiseffort is starting to pay off with the addition of a few North American customers, Kognitio is stillmainly Europe-based, with most of its customers located in the U.K. It has not significantlygrown its partnerships with service providers or the reseller agreements that would enable it todraw more on its partners to mitigate the problems with the availability of skills. While we areencouraged by Kognitio's new vision and small improvements in 2012, we believe the companyneeds to improve its execution significantly in 2013 with regard to sales and signing contracts.

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MicrosoftMicrosoft (www.microsoft.com) markets the SQL Server 2012 Fast Track reference architecture, SQLServer 2008 R2 Parallel Data Warehouse Appliance (Update 3), HP Business Decision Warehouse andDell Quickstart Data Warehouse Appliance. Microsoft does not report customer or license counts, butthere are thousands of SQL Server customers worldwide (including analytical and transactionalsystems).

Strengths

Broad market usage. With customers in the manufacturing, telecommunications, insurance,government, retail, financial services, transportation and other industries, Microsoft's solutiondemonstrates wide applicability to vertical markets. In addition, customers come from all acrossthe world, from North America to EMEA to Asia/Pacific, including countries such as Belgium,Denmark, New Zealand, South Korea, Ukraine and the U.S. Furthermore, customers reportannual revenues ranging from under $100 million to over $10 billion. Microsoft is a major rivalto any vendor just in sheer numbers of deployed data warehouses (although not in revenue,which, though rising, remains much lower).

Competitive capability and customer loyalty. Microsoft customers tend to grow withMicrosoft. Microsoft also holds its own when competing with other leading vendors, its usual


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rivals in competitive situations being IBM and Oracle. Its competitors are most often rejected onthe basis of price, which means Microsoft's pricing is considered favorable. Reference customersindicated that half of Microsoft's competitive wins were directly related to its price and ease-of-use advantages over competitors. Importantly, although customers identified issues of productmaturity (such as with metadata and monitoring tools), they considered Microsoft's skillsavailability and pricing compensated for these weaknesses — though they still encourageMicrosoft to improve on its shortcomings. In addition, Microsoft can claim the highestpercentage of customers performing data warehousing on a current software release, accordingto our survey data.

Product form factors and partners. By offering DBMS software, providing referencearchitectures, prebuilding and preloading implementations of reference architectures, offering anappliance and offering professional services (and partner connections), Microsoft offers almostevery configuration of data warehouse deployment. In addition, Microsoft has an impressive mixof partners for colead management and lead development, hardware partners, codevelopmentagreements with other suppliers and implementers, and reseller agreements. Moreover,Microsoft's Parallel Data Warehouse appliance, despite a slow start, has been adopted byapproximately 100 organizations in the past 18 months. Further adoption of this appliance islikely as Dell continues to enter the data warehouse space and sells the Dell Parallel DataWarehouse Appliance.

Completeness of solution approach. With so much of the software needed for BI and datawarehousing initiatives included in a single license, Microsoft makes the processes of purchasingand license management very easy. In addition, Microsoft offers data models (through partners)and provides assistance (directly and through partners) at the time of implementation. The mostnotable change by Microsoft in 2012 regarding data warehousing was its new vision forcombining structured and unstructured data, desktop analytics tools (such as PowerPivot),enterprise warehousing, fast implementation strategies and the cloud.

Logical data warehouse practices. Microsoft started early on unstructured and structureddata in combination by using SharePoint, PowerPivot and technology acquired from Fast Search& Transfer — all coordinated by the SQL Server engine. It has since added in-memorycapabilities across the stack by using xVelocity in SQL Server 2012, SQL Server AnalysisServices (SSAS) and PowerPivot. In addition, Microsoft has been catching up with theannouncement of HDInsight and a solution offering in partnership with Hortonworks. Finally,with Microsoft's StreamInsight complex event processing solution, customers can address thevelocity requirements of big data.

Cautions

Perceived lack of enterprise-readiness. As SQL Server grows to enable its expanded use fordata warehousing, it is generating more questions from clients about its readiness to supportlarge data warehouses and HA/DR capabilities. However, with the release of SQL Server 2012,Microsoft has continued to mature this offering with the addition of Always On features toimprove clustering, failover and active-active data warehouses. Growth in the number of largeSQL Server data warehouses will also reduce this perception.

Complete data management capabilities. SQL Server offers a complete set of datamanagement capabilities, including data integration with SQL Server Integration Services andOLAP capabilities with SSAS. However, as data warehousing projects have matured and grownin complexity, clients have reported (both through Gartner's direct interactions and through thesurvey we conducted for this Magic Quadrant) limitations with the capabilities offered with SQLServer, specifically in the areas of metadata management and data integration. Microsoft is stilldeveloping its capabilities in these areas.

Business model driven by the "majority market." Distributed process management andexecution is one aspect of the logical data warehouse. However, although Microsoft was quick torespond to its customers' fast-developing need to cope with data in high volume, in great varietyand at fluctuating velocity, and to offer integration with major Hadoop distributions, this left itbehind some of the Leaders in terms of vision. But, then, being a "fast follower" for somefeatures and functions has always been part of Microsoft's business model.

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OracleOracle (www.oracle.com) offers a range of products. Customers can choose to build a customwarehouse using Oracle's DBMS software on implementer-chosen hardware and infrastructure, use acertified configuration comprising Oracle products on Oracle-recommended hardware, or purchase anappliance or appliances ready for a warehouse design and load. Oracle offers the followingdata warehouse solutions, which are included in this evaluation: (1) DBMS software;(2) certified configurations; (3) Oracle Big Data Appliance; (4) Oracle Exadata X3 (X3-2 and X3-8);(5) Oracle SPARC SuperCluster T4 systems with Oracle Exadata Storage Servers. Oracle reports over390,000 DBMS customers worldwide (it is not possible to determine which licenses are used fortransactional systems or data warehousing systems).

Strengths

Appeal to current demand. Oracle offers data warehouse data models (for example, for retailand communications and airlines) and uses its acquisitions (such as Endeca) in applications andvertical solutions to achieve broad appeal in the data warehouse market. The breadth of Oracle'sofferings, and the fact that they meet the needs of a wide range of customers, means thatOracle scores high for execution.

DBMS market leader. Oracle's DBMS versions account for approximately 49% of the RDBMSmarket by revenue (see "Market Share: All Software Markets, Worldwide, 2011"). Overall, 73%


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of Oracle customers in our reference survey reported that they are on the current version ofOracle, and 59% reported that they will invest in additional Oracle licensing in the next 12months. We interpret this as a tendency to continue to use Oracle products. Although Oracle hasstopped officially reporting the number of Exadata machines installed, Gartner estimates it to be2,200 at the end of Oracle's fiscal 2012 (which ended on 31 May 2012).

Continual advances in features and functions. In 2012, Oracle released big data connectors(R Connector for Hadoop, an SQL connector for HDFS and Oracle Loader for Hadoop — HDFS)and spatial data and graph capability. It also launched its in-memory analytics appliance (OracleExalytics), Oracle Exadata X3 with 22.4TB of flash storage per full rack (including a Write BackFlash Cache feature for high performance) and many other innovations (see "Oracle EmbracesFlash Memory in New Exadata Database Machine"). Oracle's Big Data Appliance specificallyaddresses MapReduce using a Hadoop distribution — a key driver in the current market. Somecustomers identified ease of integration as a strength of their "all Oracle" appliance strategy.

Good penetration of most vertical markets. Oracle delivers data warehouses to many of thestandard industries for analytics: telecommunications, financial services and banking, insuranceand so on. Moreover, of all the vendors in this analysis, Oracle reports the highest incidence ofnontraditional analytics customers: sectors such as hospitality, energy trading, life sciences andfood distribution show up in its reference base. This is evidence of Oracle's ability to executewith broad appeal. Many of the vertical markets where Oracle has the greatest success containtraditional implementers or late adopters of data warehousing. Oracle's customers range inannual revenue from $100 million to over $10 billion. However, hundreds of calls to Gartner'sinquiry service show that Oracle's approach to pricing, with a variety of appliance form factorsand contract mechanisms, creates so many pricing levels that it can confuse customers.

Version-currency. Oracle customers generally report that they are on current or recentversions. This is due in part to Oracle's stringent version requirements for the maintenance ofsupport, but also because customers find migrating to the current version of Oracle extremelystraightforward (14%) or straightforward (40%), with only about one-quarter indicating thatversion migration is somewhat complex. As a side note, Oracle reports that 30% of OracleExadata customers are repeat customers, and it claims this is due to customer satisfaction.Some Oracle customers report that their version-currency is a simple response to data volumeand scaling requirements, which are partly met by keeping their version current.

Cautions

Customers' experiences of account management. Most of the reference customersidentified by Oracle (reference customers were identified by the vendors) reported that theirwarehouse has been in operation for more than a year. Only a small fraction reported going intoproduction less than six months before responding to our survey. Our survey asked thereference customers a battery of questions (couched in positive and negative terms) to elicitinformation about their overall sentiment. Oracle's customers continued to award low scores forease of use and implementation. They also continued to award it low scores for value, pricingand the overall experience. Furthermore, they reported a slight increase in technical issues, yearover year, in relation to scaling, missing functionality (unnamed) and the ability to integratewith components of the overall architecture. We should note that Oracle customers reported acontrary, positive experience when asked if there were "no issues" to report, with 20% reportingan issue-free implementation.

Customers' uncertainty about strategic compatibility. In our previous Magic Quadrant, wesaid that Oracle's appliance strategy was becoming an area of concern. We believe the appliancestrategy has become a significant influence on Oracle's approach to marketing and delivery aspart of its hardware revenue strategy. This strategy is important to Oracle, but sometimes runscontrary to customers' designated interests. Our interactions with clients indicate that the"engineered system" approach is commonly perceived as "vendor lock-in," but it is unclearwhether they consider this good or bad; it is especially important to remember that vendor lock-in with a preferred vendor in good standing is a positive thing. Customers also find itdisconcerting that some capabilities are available only with engineered systems and not withOracle's standard software products — Hybrid Columnar Compression and Smart Scan, forexample, are available only in Exadata Storage Servers. This has given some customers animpression of growing inequity in product delivery, uneven support and disparate "bestpractices." The logical data warehouse approach to analytics implies flexible, best-of-breedsolutions, so any uncertainty about which products have which features complicates decisionsabout whether to use Oracle products. It is Gartner's position that separate functionality is anatural function of building appliances ("engineered systems" as Oracle calls them) in thatappliances require and benefit from specific functionality. We raise the point here only becausecustomers may not understand that this is a predictable and normal phenomenon in matureappliance delivery. Oracle's approach serves primarily to create opportunities for it to further itsown efforts to combine hardware with software and Oracle-tuned practices. From a marketexecution perspective, this will yield positive results, but it is uncertain if the market will agreewith Oracle's vision.

Difficulty expanding large customer base. Reference customers indicated that the mostcommon reason for choosing Oracle over another vendor was simply that Oracle was theirstandard vendor, and they added that they were reluctant to access new skills to support toolsfrom other vendors. The reference survey indicates that 40% of Oracle respondents alsoconsidered IBM in a competitive situation. This rate is also borne out by Gartner's inquiry servicedata. The growing number of competitive situations shows that the default approach of adheringto simple standards is losing influence.

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ParAccel




ParAccel (www.paraccel.com) offers a high-performance analytic platform based on an MPP column-vectored database with strong in-database analytic functions. ParAccel 4.0, which shipped in August2012, remains a software-only offering, but certified configuration partnerships with Dell, Cisco, HPand NetApp, and cloud offerings with MicroStrategy and Birst, extend the company's portfolio.ParAccel claims over 60 customers worldwide, the overwhelming majority in North America.

Strengths

Ample funding and good partners. ParAccel's executive team has been in place for two yearsnow and has secured substantial new funds to support a major effort to capitalize on themarket's growing receptivity to analytic platforms that span traditional and big data initiatives.Amazon's investment, an expanding commitment by MicroStrategy, relationships of varyingdepth with Dell, NetApp and Cisco, and agreements with 42 new resellers and service providersannounced in September 2012 indicate a company determined to achieve a new level of growth.ParAccel's licensing model — a single license charge, based on intended use, regardless of typeand number of servers and storage devices — remains a useful weapon for it to use againstmany of its larger competitors.

Integration and support of analytics in a logical data warehouse. ParAccel has customerswith data warehouses that have been in production for over five years, and our interviewsrevealed satisfaction with their performance and ease of design. ParAccel 4.0 and the company'sshort-term road map continue to focus on a performance theme, but they extend it to the worldof big data by adding On Demand Integration (ODI) high-performance connectors for other datawarehouse DBMSs, Open Database Connectivity and Hadoop MapReduce, treating them asengines beneath ParAccel's highly optimized SQL analytic front end (now called the OmneOptimizer). ParAccel now supports the WITH construct in SQL syntax, a very useful capabilitythat some of its competitors still lack. It also supports Apache HCatalog, the emerging metadatafacility for the Hadoop stack.

Advanced analytics. ParAccel's expanded vision for the distributed processing dimensions ofthe logical data warehouse comes at a key moment for this market. Its support for the FinancialInformation eXchange (FIX) Protocol and New York Stock Exchange data integration willstrengthen its appeal in core vertical markets. ParAccel ships with Fuzzy Logix libraries andsupports Numerix as well, reinforcing its in-database analytics leadership; over 500 advancedfunctions are available to simplify development of complex analytics.

High-volume analytics. Customers report that ParAccel's support for multilevel, highlyrecursive analytics on self-joined datasets has been highly effective for market basket analysisand clinical trial data. Significant capabilities for social analytics and many of the new big datadataset issues are already in place, with more to come according to a road map that helpedParAccel enter the Visionaries quadrant in this year's Magic Quadrant. Its claimed support for5,000 simultaneous clients also positions it well for use by sizable enterprises in larger roles, asdoes its support for HDFS.

Cautions

Time to grow revenue. After seven years, ParAccel remains a very small company. Last year,we noted that it had significantly expanded its experienced sales staff, but growth remains slowin absolute terms. However, its latest efforts — the hiring of a senior executive and anexpansion in channel activity — should increase its appeal to large IT vendors as an alternativeto inviting a "stack" vendor into their accounts; evidence of this is clear in the strongrelationship ParAccel has formed with MicroStrategy. ParAccel has reported a significantimprovement in its sales pipeline and has substantially expanded its presence in conferencesand marketing channels. Establishing partnerships was a good first step, and in 2013 and 2014we expect to see these partnerships to produce revenue and mind share.

Possibility of loading issues. ParAccel customers continue to identify challenges with loadingand operating the database (though some of these are addressed in the 4.0 release), withHadoop ODI (which shares the processing execution and management of processing tasks withHadoop clusters), and with a new graphical user interface console. These issues have been leftlargely unaddressed by ParAccel as the company focuses on its performance-related efforts. Thissituation is somewhat uncharacteristic of a market in which data usually goes into thewarehouse without difficulty and where problems more often arise in getting it out — ParAccel,by contrast, can get any data out, but reportedly has problems loading it in the first place. Lastyear's issues, including those with online scaling and reorganization and Lightweight DirectoryAccess Protocol integration, are operability and data warehousing problems that are not alwaysa focus when analytic platform thinking is the priority. There are positive signs, however:ParAccel has stabilized operationally, as is reflected in our interviews with customers, and lastyear's reports of crashes and other maturity issues were almost absent this year.

Demand for more features. ParAccel earned some breathing room in 2011 and has anopportunity to address its issues with loading, protocol integration, online scaling andorganization. However, continued deflection of discussion about logical models and metadata willhold ParAccel back, as logical data warehouse designers think beyond execution to data curation(for example, the alignment of multitemporal interpretations and recognition of misaligned datadefinitions) and operation of the entire analytics data management fabric. Importantly, ParAcceldoes not need to provide these capabilities directly but can do so by developing partnerships,especially if it finds managed services providers in the AWS cloud.

Narrow window of cloud opportunity. The continued maturation of cloud offerings could beanother opportunity for ParAccel. MicroStrategy has made a clear commitment to ParAcceltechnology by offering its database as part of the MicroStrategy Cloud portfolio. Others couldfollow, but ParAccel's window of opportunity could be closing as its big competitors begin tomobilize, especially as most of ParAccel's initiatives have yet to take off. The company needs toaccelerate its cloud strategy and publicize some big wins. Amazon (AWS), for example, investedin ParAccel in 2011, and in 2012 announced that it had licensed ParAccel components for use in

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its planned Redshift offering, to deliver a data warehouse as a cloud service.

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SAPThis year, our analysis of SAP (www.sap.com) covers both its offerings in this market: SAP Sybase IQand SAP Hana. SAP Sybase IQ, was the first column-store DBMS, and is SAP/Sybase's primary datawarehouse DBMS. It is available as a stand-alone DBMS, a data warehouse appliance and on an OEMbasis through system integration vendors (such as BMMsoft). SAP Sybase IQ has over 2,000customers worldwide. SAP Hana, the company's in-memory DBMS, became generally available inJune 2011, and we estimate it had about 500 customers at the end of June 2012 and about 1,000 atthe end of December 2012.

Strengths

In-memory leader. SAP leads the market with its messaging about in-memory computing,driven by the SAP Hana (which attained general availability in 2011) offering. Customer uptakeof SAP Hana has been fast: it attracted 500 customers in just 12 months, a great achievementfor a new product. The two use cases that have driven adoption of SAP Hana are in-memorydata marts and SAP Business Information Warehouse (BW) powered by Hana. SAP continues toexecute on its in-memory strategy, with Hana also supporting other products.

Support for the logical data warehouse. The release of SAP Sybase IQ 15.4 in 2011 movedSAP Sybase IQ from the traditional data warehouse sector toward the logical data warehousespace by adding support for big data and the ability to create separately managed storage andprocessing virtual work units that are managed by the DBMS (it already had substantial mixed-workload management features, faster loading capabilities, query parallelism and support for in-DBMS MapReduce capabilities and connectors to Hadoop). SAP Sybase IQ in combination withSAP Hana's in-memory capabilities, R integration and text analytics offers customers tools withwhich to build a logical data warehouse architecture supporting a wide range of use cases. Thecompany's vision of an "SAP real-time data platform" goes beyond current demand in the datawarehouse DBMS market by including the SAP Business Suite and real-time analytics capabilitiesfor transactional data.

Effective sales and marketing. SAP led the market for DBMS revenue growth in 2011, with a48% increase year over year, which was much higher than the overall market's 14.8% rise. Thissuccess shows that SAP's strategy to become a leading player is having an impact on the DBMSmarket (see "Forecast: RDBMS by Operating System, Worldwide, 2010-2015").

Cautions

Confusing data warehousing strategy. Throughout 2012, Gartner's interactions with SAPcustomers showed they were unsure whether to use SAP Sybase IQ or SAP Hana for datawarehousing. Although SAP's messaging articulated the relative positions of SAP Sybase IQ andSAP Hana as part of a SAP real-time data platform, Gartner knows of customers that havecompared SAP Hana with products from other vendors when SAP Sybase IQ would have been abetter alternative.

Newness of SAP Hana. Our customer survey indicates that although customers are verysatisfied with SAP Hana's performance and capabilities, as a new product it comes with expectedchallenges, such as those relating to stability and overall maturity. Our interactions with clientshave also revealed that SAP Hana deployments remain small (for less than 1TB of data). SAPstates that SAP Hana deployments are split between SAP BW powered by Hana (45%), SAPHana data marts (45%), and other uses (10%). SAP has pursued an aggressive and responsiveproduct release schedule to mature and evolve the SAP Hana platform, with five major servicepack releases offering significant new features introduced in a single year (the latest, SPS5,arrived in November 2012).

Concerns about ease of implementation and use. SAP's customer survey ratings for ease ofimplementation, ease of use and satisfaction with support reveal that the company needs tomake overall improvements. Although satisfaction with SAP's professional services scoredbetter, this did not fully compensate for the other ratings. Although these ratings may be partlydue to the fact that SAP Hana is a recent product and although it will doubtless improve overtime, SAP still needs to focus on remedying it shortcomings. Then again, these do not seem tohave been strongly limiting factors so far, given the strong adoption of Hana.

Little interest in big data among SAP customers. SAP's customer base shows the lowestrate of adoption of big data technologies such as NoSQL, Hadoop MapReduceand processingclusters. The behavior of its customers indicates that they are not pursuing big data projects atthe same rate as those of other vendors. This could create a false impression of the market'sdemand — it could be misinterpreted as an indication that big data technology is not wanted.

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TeradataTeradata (www.teradata.com) has a 30-year history in the data warehouse market, which it supplieswith a combination of tuned hardware and analytics-specific database software. With a variety ofproducts, which include the Teradata database (version 14.0 was released February 2012) on variousappliance form factors and the Aster Database (available as a DBMS, an appliance or via the cloudusing the Amazon Elastic Compute Cloud), Teradata offers both traditional and emerging logical datawarehouse solutions. Teradata continued to grow in 2012, and it claims over 1,200 customersworldwide, almost all for production data warehouses.

Strengths


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Market presence and experience. Teradata delivers its products in such a way that it alwaysleads, or is at least competitive, in overall execution and constantly pushes the market towardemerging best practices and product innovation. Teradata's traditional market includes advanceddata warehousing. Dominant Teradata customers in this field are analytics organizations withsignificant data warehousing requirements that use many of Teradata's built-in functions. Therecent influx of inexperienced practitioners creates a demand for education as users'requirements quickly outpace the initial expectations of their organizations when implementingdata warehouses. For Teradata, however, this is familiar ground, as it has over 30 years'experience of solving problems with its management tools and of load-balancing systems.Teradata also has an opportunity to win new customers from this base of newcomers as theydevelop advanced (and well-known) requirements — and discover there are reasons for all thetechnologies that comprise a data warehouse.

Product diversity and growth of features and functions. Teradata has four differentappliance form factors, ranging from a tiny proof of concept (POC) solution up to an all-flash-memory, enterprise-capable solution. Recent additions to the company's road map and theongoing incorporation of acquired intellectual property (such as Unity) give Teradata the edgeover most of its competitors. Its family of products is further expanded by the Teradata AsterBig Analytics Appliance, a packaged offering combining the Aster Database and HortonworksData Platform, a Hadoop distribution. This product diversity firmly addresses the market's bigdata demands and creates an educational path to guide new entrants to this field.

Vision for the logical data warehouse. Teradata is a leader in terms of vision for the logicaldata warehouse with its Unified Data Architecture that combines Teradata, Aster and Hadooptechnology and is complemented by its Unity products. The Hortonworks partnership for Hadoopsupports a wide variety of data and the changing nature of metadata across various use cases.The Teradata Aster Big Analytics Appliance is starting to address metadata managementchallenges that are central to the logical data warehouse with support for Apache HCatalog; thisis helping Teradata make it easier to share and reuse data stored in external file systems suchas HDFS.

Customer loyalty and durability of purchases. Our survey of Gartner clients and referencecustomers shows that Teradata's customers have been using Teradata for periods ranging from10 months to over 10 years. We also acknowledge that Teradata has a multiyear track record ofdurable implementations, customer satisfaction, and upgrades across different generations ofhardware and software.

Diversity of industries and wide geographic coverage. Healthcare, transportation,financial, insurance, telecommunications, manufacturing, retail, IT services and media areamong the industries that Teradata supports. Additionally, its customer base includesorganizations with annual revenues ranging from $200 million to $20 billion and more. Nearly allof Teradata's surveyed customers reported that it was their de facto standard vendor for datawarehouse products. Teradata is present in every world region, with direct or appropriatelycapable local support available to customers.

Upgrades and staffing requirements. Teradata customers reported they incurred either lowor no additional staffing costs when implementing its products, indicating that it is easy to movefrom an older version to a new version. A small but meaningful number of references reported atemporary 50% increase in staffing costs when performing upgrades, but the same customerssaid they used the temporary increase in staff numbers as an opportunity to change their datawarehouse's design.

Cautions

Unexplained resistance to version upgrades. Almost 50% of Teradata's referencecustomers, and a similar number of Teradata customers that use Gartner's inquiry service,report delaying upgrades to new versions. Although they give no reason for this (recent) trend,other survey questions and inquiry questions indicate that Teradata's costs are coming underincreasing scrutiny. However, many data warehouse managers report this is because Teradataappears as a specific line-item cost in their budget for the analytics use case. Nevertheless,customers do report that this scrutiny is causing concerns — not because the cost is high, butbecause it stands out — and the recent high number of "not on current version" responses mightreflect these concerns. The reluctance to upgrade might also be due to the ability of olderversions to continue to meet expected performance requirement, so reducing the urgency ofupgrades or physical scale-outs.

Near absence of skilled staff. Over half Teradata's reference customers and a similarpercentage of Teradata clients who use Gartner's inquiry service indicate that the market doesnot have enough skilled Teradata professionals. This results in clients complaining that their useof third-party database administration tools, which are difficult to use with Teradata, makes iteven more challenging to scale their internal teams to support Teradata products. This could bea major issue for Teradata when competing with other leading vendors for dominance inemerging logical data warehouse practices and even in traditional data warehouses. Skilledpersonnel with deep knowledge of competing technologies are widely available for customers ofthose vendors.

Support for extended environments. A small but meaningful minority of Teradata'scustomers reported issues with new features (for example, temporal features), long durationsfor migrations, and the use of Teradata's other offerings (such as Aprimo). In light of the skillsissue, Teradata risks offering even more advanced products while having even less ability tosupport them in the broad market.

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Vendors Added or Dropped




We review and adjust our inclusion criteria for Magic Quadrants and MarketScopes as marketschange. As a result, the mix of vendors in a Magic Quadrant or MarketScope may change over time. Avendor's appearance in a Magic Quadrant or MarketScope one year and not the next does notnecessarily indicate that we have changed our opinion of that vendor. It may reflect a change in themarket, and therefore changed evaluation criteria, or a change of focus by that vendor.

Exclusion from the Magic Quadrant should not disqualify a vendor or its products from consideration.Customers should examine vendors and products in light of their specific needs and circumstances.

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AddedCalpont

Gartner's Magic Quadrant process includes research on a wider range of vendors than appear in thepublished document. This wide remit enables us to spot vendors that need to be added to the MagicQuadrant, as in the case of Calpont this year.

There are many vendors of data warehouse DBMSs in addition to those featured in this MagicQuadrant. However, they did not meet our inclusion criteria. Excluded vendors often also participatein appliance form factors, but do not actually produce their own DBMS — instead they offer othervendors' products on an OEM basis. Some offer technological solutions but failed to meet ourreference requirements (by not providing enough active references, whether because they are newcompanies with few customers or for other reasons). Others provide a range of functionality, butoperate only in a specific technical environment. Still others operate only in a single region or supportonly narrow, departmental implementations. Another reason for excluding some vendors was that,although they met all the functional, deployment and geographic requirements, as very new entrantsthey have limited revenue and few production customers.

In addition to the vendors featured in this Magic Quadrant, Gartner clients sometimes consider thefollowing vendors, when their specific capabilities match the deployment needs (this list also includesrecent market entrants with relevant capabilities). Unless otherwise noted, the information providedon these vendors derives from responses to Gartner's initial request for information for this documentor from reference survey respondents. This following list is not intended to be comprehensive:

BMMsoft. The BMMsoft EDMT solution includes the SAP/Sybase IQ database as its datamanagement base system, and as BMMsoft is an OEM supplier, it is not considered a stand-alone DBMS. Nevertheless, prospective clients should note that the product has unstructureddata, content, email analytics and other capabilities in addition to the DBMS. BMMsoft is basedin San Francisco, California.

Objectivity. This vendor, which has a lineage dating back to 1989 when it first began to offeran object-oriented database, now offers InfiniteGraph and Objectivity/DB (version 10.2.1). Itclaims to have thousands of direct licensed customers as well as thousands of embeddedlicenses worldwide. Objectivity did not supply the minimum number of reference customercontacts to be included in this research. It is based in Sunnyvale, California.

ParStream. ParStream 1.8 became available in June 2012 (the first version launched in October2011) and offers a High Performance Compressed Index (HPCI) on an MPP architecture.However, ParStream did not have enough production customers to qualify for this research. It isbased in Cologne, Germany and Redwood City, California.

XtremeData. This privately owned company targets organizations that need a massivelyscalable DBMS solution for mixed read and write workloads in the cloud. Organizations pay fortheir usage with elastic scalability, and they benefit from low entry costs and fast time-to-market. XtremeData is available on AWS and other clouds. Our information on this vendor wasobtained not from the RFI we sent to many vendors or from reference customers but from directcommunications with XtremeData's technical personnel and the ongoing research of Gartneranalysts. XtremeData is based in Schaumburg, Illinois.

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DroppedSAND Technology. We were unable to contact anyone willing to take responsibility forcommunications at this vendor. We were also unable to collect sufficient information, includingclient references and details of market, product road map and strategic direction, to includeSAND Technology in this Magic Quadrant.

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Inclusion and Exclusion CriteriaTo be included in this Magic Quadrant vendors had to meet the following criteria:

Vendors must have DBMS software that has been generally available for licensing or supporteddownload for at least a year (that is, since 10 December 2011).

We use the most recent release of the software to evaluate each vendor's current technicalcapabilities. We do not consider beta releases. For existing data warehouses and directvendor customer references and reference survey responses, all versions currently used inproduction are considered. For older versions, we consider whether later releases mayhave addressed reported issues, but also the rate at which customers refuse to move to






newer versions.

Product evaluations include technical capabilities, features and functionality present in theproduct or supported for download through midnight, U.S. Eastern Daylight Time on 7December 2012. Capabilities, product features or functionality released after this date canbe included at Gartner's discretion and in a manner Gartner deems appropriate forensuring the quality of our research product on behalf of our nonvendor clients. We alsoconsider how such later releases can reasonably impact the end-user experience.

Vendors must have generated revenue from at least 10 verifiable and distinct organizations withdata warehouse DBMSs in production that responded to Gartner's approved reference surveyquestionnaire. Revenue can be from licenses, support and/or maintenance. Gartner may includeadditional vendors based on undisclosed references in cases of known use for classified butunspecified use cases. For this year's Magic Quadrant, the approved questionnaire was producedin English only.

Customers in production must have deployed data warehouses that integrate data from at leasttwo operational source systems for more than one end-user community (such as separatebusiness lines or differing levels of analytics).

Support for these data warehouse DBMS product(s) must be available from the vendor. We alsoconsider products from vendors that control or participate in the engineering of open-sourceDBMSs and their support. We also include vendors' capabilities to coordinate data managementand processing from additional sources beyond the DBMS, but continue to require that a DBMSmeets Gartner's definition (see Notes 1 to 4 for defining parameters).

Vendors participating in the data warehouse DBMS market must demonstrate their ability todeliver the necessary services to support a data warehouse via the establishment and delivery ofsupport processes, professional services and/or committed resources and budget.

Products that exclusively support an integrated front-end tool that reads only from the paireddata management system do not qualify for this Magic Quadrant.

For details of our research methodology, see Note 5.

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Evaluation Criteria

Ability to ExecuteAbility to Execute is primarily concerned with the ability and maturity of the product and the vendor.Criteria under this heading also consider the product's portability, its ability to run and scale indifferent operating environments (giving the customer a range of options), and the differentiationbetween data warehouse DBMS solutions and data warehouse appliances. Ability to Execute criteriaare critical to customers' satisfaction and success with a product, so customer references areweighted heavily throughout.

Product/service includes the technical attributes of the DBMS, as well as features and functionalitybuilt specifically to manage the DBMS when used as a data warehouse platform. We include HA/DR,support and management of mixed workloads, the speed and scalability of data loading, and supportfor new hardware and memory models. These attributes are evaluated across a variety of databasesizes and workloads. We also consider the automated management and resources necessary tomanage a data warehouse, especially as it scales to accommodate larger and more complexworkloads. The abilities to respond to new varieties of information assets, incorporate distributedprocessing and manage distributed data are also part of this criterion.

Overall viability includes corporate aspects such as the skills of the personnel, financial stability,research and development (R&D) investment, and merger and acquisition activity. It also covers themanagement's ability to respond to market changes and the company's ability to survive marketdifficulties (crucial for long-term survival). Vendors are further evaluated on their capability toestablish dominance in meeting a specific market demand.

Under sales execution/pricing we examine the price/performance and pricing models of the DBMS,and the ability of the sales force to manage accounts (judging from feedback from our clients). Wealso consider DBMS software's market share. Also included is the diversity and innovative nature ofpackaging and pricing models, including the ability to promote, sell and support the product aroundthe world.

Market responsiveness and track record — for example, the vendor's ability to meet demand forvertical-market solutions, general market perceptions of the vendor and its products, and thediversity of its delivery models in response to demand (for example, its ability to offer appliances,software solutions, data warehouse as a service offerings or certified configurations). We alsoconsider the vendor's ability to adapt to market changes during the previous 18 months and itsflexibility in response to market dynamics over a longer period.

Marketing execution includes the ability to generate and develop leads, channel developmentthrough Internet-enabled trial software delivery, partnering agreements (including co-seller, co-marketing and co-lead management arrangements). Also considered are the vendor's coordinationand delivery of education and marketing events throughout the world and across vertical markets.

The customer experience criterion is based primarily on customer reference surveys anddiscussions with users of Gartner's inquiry service during the previous six quarters. Also consideredare the vendor's track record of POCs, customers' perceptions of the product, and customers' loyaltyto the vendor (this reflects their tolerance of its practices and can indicate their level of satisfaction).




This criterion is sensitive to year-to-year fluctuations, based on customer experience surveys.Additionally, customer input regarding the application of products to limited use cases can besignificant, depending on the success or failure of the vendor's approach in the market.

Operations covers the alignment of the vendor's operations, as well as whether and how thisenhances its ability to deliver. Aspects considered include field delivery of appliances, manufacturing(including the identification of diverse geographic cost advantages), internationalization of theproduct (in light of both technical and legal requirements) and adequate staffing.

Table 1. Ability to Execute Evaluation Criteria

Evaluation Criteria Weighting

Product/Service High

Overall Viability (Business Unit, Financial, Strategy, Organization) Low

Sales Execution/Pricing Standard

Market Responsiveness and Track Record High

Marketing Execution High

Customer Experience High

Operations Low


Completeness of VisionCompleteness of Vision encompasses a vendor's abilities to understand the functions needed tosupport data warehouse workload design, develop a product strategy that meets the market'srequirements, comprehend overall market trends, and influence or lead the market when necessary.A visionary leadership role is necessary for the long-term viability of both product and company. Avendor's vision is enhanced by its willingness to extend its influence throughout the market byworking with independent third-party application software vendors that deliver data-warehouse-driven solutions (for BI, for example). A successful vendor will be able not only to understand thecompetitive landscape of data warehouses but also to shape the future of this field.

Market understanding covers a vendor's ability to understand the market and shape its growth andvision. In addition to examining a vendor's core competencies in this market, we consider itsawareness of new trends, such as the increasing sophistication of end users, the growth in datavolumes, and the changing concept of the data warehouse and analytics information management inthe enterprise (see also "Data Warehousing Trends for the CIO, 2011-2012").

Marketing strategy refers to a vendor's marketing messages, product focus, and ability to chooseappropriate target markets and third-party software vendor partnerships to enhance the marketabilityof its products. For example, we consider whether the vendor encourages and supports independentsoftware vendors in its efforts to support the DBMS in native mode (via, for instance, co-marketing orco-advertising with "value-added" partners). This criterion includes the vendor's responses to themarket trends identified above and any offers of alternative solutions in its marketing materials andplans.

Sales strategy is an important criterion. It encompasses all channels and partnerships developed toassist with selling, and is especially important for younger organizations as it can enable them greatlyto increase their market presence while maintaining lower sales costs (for example, through co-sellingor joint advertising). This criterion also covers a vendor's ability to communicate its vision to its fieldorganization and, therefore, to clients and prospective customers. Also included are pricinginnovations and strategies, such as new licensing arrangements and the availability of freeware andtrial software.

Offering (product) strategy covers the areas of product portability and packaging. Vendors shoulddemonstrate a diverse strategy that enables customers to choose what they need to build a completedata warehouse solution. Also covered are partners' offerings that include technical, marketing, salesand support integration. Additionally, we consider the availability of certified configurations andappliances based on the vendor's DBMS.

Business model covers how a vendor's model of a target market combines with its products andpricing, and whether the vendor can generate profits with this model, judging from its packaging andofferings. Additionally, we consider reviews of publicly announced earnings and forward-lookingstatements relating to an intended market focus. For private companies and to augment publiclyavailable information, we use proxies for earnings and new customer growth, such as the number ofGartner clients indicating interest in, or awareness of, a vendor's products during calls to our inquiryservice.

In recent years, organizations seeking traditional data warehousing solutions have become interestedin vertical/industry strategy. This strategy affects a vendor's ability to understand its clients.Aspects such as vertical-market sales teams and specific vertical-market data models are consideredfor this criterion.

Innovation is a major criterion when evaluating the vision of data warehouse DBMS vendors indeveloping new functionality, allocating R&D spending and leading the market in new directions. This



criterion also covers a vendor's ability to innovate and develop new functionality in its DBMS,specifically for data warehouses. The use of new storage and hardware models is one key to this.Users expect a DBMS to become self-tuning, reducing the resources required to optimize the datawarehouse, especially as mixed workloads increase. Also addressed here is the maturation ofalternative delivery methods, such as IaaS and cloud infrastructures. Vendors' awareness of new datawarehousing methodologies and delivery trends is also considered.

We evaluate a vendor's worldwide reach and geographic strategy by considering its ability to useits own resources in different regions, as well as those of subsidiaries and partners. This criterionconsiders a vendor's ability to support clients throughout the world, around the clock and in manylanguages. Anticipation of regional and global economic conditions is also considered.

Table 2. Completeness of VisionEvaluation Criteria

Evaluation Criteria Weighting

Market Understanding High

Marketing Strategy Standard

Sales Strategy Standard

Offering (Product) Strategy High

Business Model Low

Vertical/Industry Strategy Low

Innovation High

Geographic Strategy Standard


Quadrant Descriptions

LeadersThe Leaders quadrant generally contains the vendors that demonstrate the greatest support for datawarehouses of all sizes, with large numbers of concurrent users and management of mixed datawarehousing workloads. These vendors lead in data warehousing by consistently demonstratingcustomer satisfaction and strong support, as well as longevity in the data warehouse DBMS market,with strong hardware alliances. Hence, Leaders represent the lowest risk for data warehouseimplementations in relation to, among other things, performance as mixed workloads, database sizesand complexity increase. Additionally, the market's maturity demands that Leaders maintain a strongvision for the key trends of the past year: mixed-workload management for end-user service-levelsatisfaction and data volume management. Finally, Leaders have the ability to align their messaging,product placement and pricing models to support both traditional data warehousing practices andemerging practices for distributed processing, data virtualization, metadata management anddynamic optimization, which are essential for the logical data warehouse and big data to become the"new normal." Importantly, with so many Leaders aligned with our current criteria, the scale ofdifferentiation will shift even more strongly in coming years toward combining structured andunstructured analytics, support for external files and processing, and the ability to support a diverseset of end-user applications via metadata and performance auditing and tuning.

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ChallengersThe Challengers quadrant includes stable vendors with strong, established offerings but a relative lackof vision — in 2012 it was possible for very strong traditional vendors to have high scores for Abilityto Execute but lower scores for Completeness of Vision. Challengers have presence in the datawarehouse DBMS space, proven products and demonstrable corporate stability. They generally have ahighly capable execution model. Ease of implementation, clarity of message and engagement withclients contribute to these vendors' success. Challengers in this year's Magic Quadrant focus onsuperior execution relative to pricing; with customers demanding discernible value for price paid,Challengers offer simplicity, fast implementation and less functionality. Organizations often purchaseChallengers' products initially for limited deployments, such as a departmental warehouse or a largedata mart, with the intention of later scaling them up to enterprise-class deployments. Increasingly,Gartner clients consider exceptional performance on structured data only to be evidence of "limited"vision.

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VisionariesVisionaries take a forward-thinking approach to managing the hardware, software and end-useraspects of a data warehouse. However, they often suffer from a lack of a global, and even strongregional, presence. Frequently, their messaging to align benefits with market use cases is unclear.They normally have smaller market shares than Leaders and Challengers. New entrants withexceptional technology may appear in this quadrant soon after their products become generallyavailable. But, more typically, vendors with unique or exceptional technology appear in this quadrantonce their products have been generally available for several quarters. The Visionaries quadrant is





often populated by entrants with increasing functional capabilities combined with new concepts thatare unproven in the market.

To qualify as Visionaries, vendors must demonstrate that they have customers in production, in orderto prove the value of their functionality and/or architecture. Our requirements for productioncustomers and general availability for at least a year mean that Visionaries must be more than juststartups with a good idea. Frequently, Visionaries will drive other vendors and products in this markettoward new concepts and engineering enhancements. During 2011, the Visionaries quadrant waspopulated with a larger number of vendors because overall market demand for strategies for specificfunctions was not being met. But many vendors answered that demand partially or fully in 2012 —such as the use of MapReduce for large-scale data analytics and massive process scaling inheterogeneous hardware environments.

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Niche PlayersNiche Players generally deliver a highly specialized product with limited market appeal. Frequently, aNiche Player provides an exceptional data warehouse DBMS product, but is isolated or limited to aspecific end-user community, region or industry. Although the solution itself may not have limitations,adoption is limited.

This quadrant contains vendors in several categories:

Those with data warehouse DBMS products that lack a strong or a large customer base.

Those with a data warehouse DBMS that lacks the functionality of those of the Leaders.

Those with new data warehouse DBMS products that lack general customer acceptance or theproven functionality to move beyond niche status. Niche Players typically offer smaller,specialized solutions that are used for specific data warehouse applications, depending on theclient's needs.

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ContextIn 2012 the data warehouse began to fulfill its new role as an overall analytics data managementplatform, and we expect this to continue in 2013. Gartner predicts that from 2015 "big data" conceptswill become part of the "new normal" in analytics and data management and integration, andsolidified as pro forma requirements by 2018. In "Big Data Drives Rapid Changes in Infrastructureand $232 Billion in IT Spending Through 2016," we state that big data will cease to be adistinguishable technological approach in 2015 and that it will be wholly obscured by 2018. However,for now, big data remains a specific requirement that the data warehouse must address as it evolvesinto a new form — the logical data warehouse.

In addition, organizations demanding advanced capabilities have amplified their requirements toinclude extended query-driven analytics, time series analysis and causal analysis of extended datastructures beyond relational data. Distributed processing, which is best represented by MapReducedistributed processing solutions and solutions that emulate this type of processing (such as those ofCloudera, Hortonworks, MapR and even LexisNexis's High Performance Computing Cluster), are risingin demand. Organizations that were previously content to complete such distributed processes onseparate clusters of servers and storage devices now demand that this capability is integrated withexisting analytic engines. In some cases, BI platforms are being used to address this demand, as aredata virtualization tools.

In 2012, many data warehouse programs actually regressed in terms of capability and demand. Thus,our Leaders are a mixed group, some with technology-leading vision across the board, and othersmore focused on meeting technologically regressive demand. It is important to remember thatmarket leadership does not necessarily equate to technology leadership. As big data becomes"normal" and the logical data warehouse becomes the best practice, vision will become increasinglyimportant. As the market continues to evolve, we expect to see a significant split between vendorsthat execute strongly in fulfillment of less sophisticated demand and vendors with the vision toprovide advanced technologies for leading-edge implementers.

Increasingly, for reasons of HA, visibility into performance, resource management and otherdemands, data warehouse DBMS vendors are being required to address enterprise-readiness by"hardening" extended environments. Many of these vendors have already adopted practices anddeployed features and functions to support big data management and processing as well as thevirtualization capabilities required for new analytics practices — but they are ahead of marketadoption in this respect. Being ahead of demand indicates strong vision.

This Magic Quadrant deals with the key capabilities of business analytics and information capabilitiesframeworks. It should therefore interest anyone involved in defining, purchasing, building ormanaging a data warehouse environment — notably CIOs, chief technology officers, members of BIcompetency centers, infrastructure, database and data warehouse architects, database administratorsand IT purchasing managers.

For this Magic Quadrant, Gartner based most of its analysis on information collected betweenDecember 2011 and November 2012. But we also considered information from a longer historicperiod, as well as any late-breaking news relating to vendors' products, customers and finances.

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Market OverviewIn 2012, the DBMS market revenue continued to grow. The traditional vendors plugged most of thegaps in their product portfolios for big data and distributed processing. In the process they regainedmuch of the demand that had dropped away in the previous year. Much remains to be done,however, in relation to customers' demands for new data warehouse practices and capabilities. Forexample, vendors need to offer customers the ability to shift between relational, virtual anddistributed data management and processing seamlessly, rather than via sometimes clumsy manualchange-overs.

Additionally, the market recognized that a data warehouse need no longer be defined by only by aDBMS running a primary repository. Data warehouses now can include virtual data stores, federationand even distributed processing across clusters. We use the term "logical data warehouse" to refer tothese new data warehouses and the practices associated with them.

Incorporating data stored in HDFS and emerging NoSQL stores such as MongoDB and Couchbase willbecome increasingly common. NoSQL data stores are typically used by programmers, not "databasepeople," so care is required to ensure data governance is not circumvented and users of the new toolsare brought into the data management community along with the development of the logical datawarehouse.

Importantly, we expect the DBMS market to have grown by about 10% in 2012, despite the troubledeconomic situation (see "Forecast Analysis: Enterprise Infrastructure Software, Worldwide, 2011-2016, 2Q12 Update"). This underlines the investment organizations are making in informationmanagement technologies.

While leaders in data warehouse implementation and utilization are demanding new architectureswith extended data management capabilities, many entirely new users are at long last entering thedata warehouse market. In 2012 Gartner recorded a significant increase in inquiries fromorganizations seeking to deploy data warehouses and analytic data stores for the first time. (Thismight sound incredible over 25 years into the data warehousing era, but we asked these clientsseveral questions to confirm that they were contemplating truly "greenfield" data warehousinginitiatives.)

There is cause for concern about these new implementations. History shows the periodic resurgenceof discredited practices by disreputable or ignorant implementers, which have led organizations' datawarehouse practices astray. At the same time, vendors have an opportunity to oversell infrastructure.Clients should therefore be cautious and conduct POC exercises that reflect their expected use caseover the next three to five years (no longer than this because use cases will grow and require newtechnology, not oversized aging technology). Additionally, new adopters of data warehousingtechnology should carefully evaluate how their workload changes and could be managed by differentsolution architectures. Large-volume queries on structured data have very different performancerequirements and data design from highly recursive queries and subselection analysis, and these inturn differ from blended structured, unstructured and distributed data processes. Organizations newto this field must learn the lessons of the past 25 years, not repeat the painful mistakes of theirpredecessors.

For most of the past 20 years, fewer than 20% of organizations have tended to adopt visionaryarchitectures five to seven years before they became widely accepted. In 2012, however, theemergence of the logical data warehouse — and its associated set of best practices for warehousingand analytics data management — had a significant impact on customers' vision. Gartner clientsincreasingly reported that the logical data warehouse was their preferred future architecture for datawarehouses. Clearly, an effective data warehouse remains a mission-critical requirement.

One alternative to the logical data warehouse as an analytics information architecture is wholesalereplacement of the data warehouse with search, content analytics and MapReduce clusters, and theelimination of the centralized repository. The year also saw the rise of the concept of cloud-deployedanalytics data management (sometimes referred to as data warehouses, sometimes contrasted withthem).

In light of these developments, it is self-serving and disingenuous, even intentionally misleading, tocontinue to promote the idea of a centralized repository as the only solution for data warehousing. Adata warehouse is first and foremost a data integration and rationalization engine. It is becomingincreasingly important for vendors and implementers to recognize that a data warehouse usesmultiple technologies. The misdirection only grows worse when proposals are made to replace entirearchitectures based solely on the excuse of adding one new type of technology (for example, NoSQLor MapReduce). However, the impact of the information management challenges arising from whatGartner calls the Nexus of Forces — Cloud, Social, Mobility and Information — is undeniable.

In 2012, Gartner observed that IBM, Oracle and Teradata began to deliver, in very different fashion,on their visions and road maps for revised offerings. It is becoming increasingly difficult for thesevendors to demonstrate a clear lead over each other as they each have an advantage in differentareas of analysis, and they all struggle to sell to each other's customers while easily defending theirown customer base. It is entirely possible for one of them to lead in terms of execution by deliveringonly to the more traditional customers, and for another to lead the market toward a new vision of, forexample, logical data warehouse practices.

Microsoft began to deliver on its vision for the data warehouse market in 2012. Implementersresponded with increased interest, judging from the number of inquiries Gartner received.

Meanwhile, SAP completed the transition of Sybase from acquisition to fully enabled technology



delivery unit within the overall SAP corporation. SAP's Data Services, BI platform and Hana were thesubject of more frequent calls to Gartner's enquiry service from organizations seeking advice aboutcompetitive situations, so these offerings are looming large on the horizons of the market's traditionalleaders.

2012 also brought the return to the Visionaries quadrant of Paraccel and Kognitio. However, theircontinued success will depend on their execution in 2013, rather than their improved vision.

Actian, for its part, recovered in terms of both vision and execution. Its aggressive positioning helpedexpand the customer base for its new products.

1010data branched into new vertical markets and has therefore improved its position on theCompleteness of Vision axis. But 1010data remained somewhat difficult to position on the MagicQuadrant as it differs considerably from other vendors in that, although it offers a DBMS, its strongestoffering is an outsourced and complete technology stack.

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magic quadrant for business intelligence and analytics platforms - january

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