denodo data virtualization...
TRANSCRIPT
Denodo Data Virtualization Platform
February 2019
Data VirtualizationOverview
Company confidential – do not forward or distribute3
The Data ChallengeGrowth of Disparate Data that is hard to Access and Integrate
Difficult to integrate disparate data that is located on-premises and in cloud data sources.
Traditional Integration cannot keep up with dynamic changes in data sources and business requirements.
It is difficult to maintain consistent real-time data access and governance policies across data siloes.
Traditional data integration is complex. The time to market for new data projects is too slow.
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The Business Need
Real-Time Access to Enterprise Data to Support Business Processes
Cloud modernization and analytics for agility, flexibility, and scalability
M & A activities and integrating data across business units
Comply with industry specific or regional regulatory requirements
Streamline operational and supply chain processes to reduce costs
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Consumein business
applications
Combinerelated data
into views
2
3 DATA CONSUMERS
Enterprise Applications, Reporting, BI, Portals, ESB, Mobile, Web, Users, IoT/Streaming Data
Connectto disparate
data sources
1 DISPARATE DATA SOURCES
Databases & Warehouses, Cloud/Saas Applications, Big Data, NoSQL, Web, XML, Excel, PDF, Word...
Less StructuredMore Structured
Multiple protocols,formats
Linked data servicesquery, search, browse
Request/Reply,event driven
Securedelivery
Library of wrappers
Webautomation
Any data or content
Read & Write
DATA VIRTUALIZATION
DATA CONSUMERSAnalytical Operational
CONNECT COMBINE CONSUMEShare, Deliver,
Publish, Govern, Collaborate
Discover, Transform,
Prepare, Improve Quality, Integrate
Normalized views of
disparate data
Agile Development
Performance
Resource Management
Lifecycle Management Data Services
Data Catalog
Governance & Metadata
Security & Data Privacy
Data Virtualization is the Solution
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Benefits of a Hybrid Data Fabric
6 Key Benefits
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Hybrid Data Fabric
Customer Example – Autodesk
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Hybrid Data Fabric
Customer Example – Asurion
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Guiding principles on when to use DV versus other methods for data delivery are driven by your non-
functional requirements (use cases, TCO, time-to-market)
Data Virtualization, ETL, ESB Compared
Physical Movement and Consolidation
Logical Abstraction and Virtual Integration
Synchronization and Propagation
ETL CDC
DB DB DB DB
Scheduled Event Driven
▪ Building DWs and MDM Hubs▪ Complex workflows and DQ▪ Historical data and cubes
Data Virtualization
DB Applications
On demand
▪ Distributed access and delivery▪ Agility and timeliness▪ Logical Data Warehouse
EAI / ESB
Application Application
Event Driven
▪ Business process automation▪ Transaction propagation▪ Messaging with small payloads
MIDDLEWARE
PURPOSE
MODEL
STRENGHTS
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Data Integration Use Case SummaryUse Case DV ETL ESB
Moving data into EDW or ODS ✔
Migrating EDW (to Cloud) ✔ ✔
Data Unification ✔
Customer 720º ✔
Real-time insights ✔ ✔
Agile Data Marts ✔
Physical Data Marts ✔
Agile Reporting (from EDW + other sources) ✔
Logical Data Warehouse ✔
Data Warehouse Offloading ✔ ✔
Application Synchronization ✔ ✔
Metadata Discovery an Enrichment ✔
Self-Service Analytics ✔
ETL “seeding” (decouple ETL from sources) ✔
Event-Driven Workflows ✔
Single Sign-On Security – Data Access by Role ✔
DV and ETL used in conjunction for solution
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Data Virtualization, Federation, ETL, ESB Compared
Virtualization Federation ETL ESB
Data abstraction Full Partial Partial Full
Robust Performance FullLimited to a few data
sourcesPrimarily in Batch mode
Limited to few data sources
Zero replication Full Partial None Partial
Real-time Information FullLimited to a few data
sourcesPrimarily Batch
Limited to few data sources
Self-service data services Full None None Partial
Centralized metadata, security, and governance
Full None Partial None
Solutions Denodo, Cisco, RedHatTableau, QueryGrid,
SAP SDA
Informatica PowerCenter, IBM DataStage, Talend
Data FabricTIBCO ESB, Mulesoft
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The Enterprise Data Layer for Big Data and Analytics
Reference Architecture
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Operational Systems Reference ArchitectureData Sources
Data Warehouse
Enterprise Applications
Web, Cloud,SaaS
Data Abstraction &
Delivery
Optional:
API Mgmnt.and/or
EnterpriseService
Bus (SOA)
Service Consumers
EnterpriseApplications
Service Providers
Mobile
Web
Dat
a Se
rvic
es
Security &Governance
Ab
stra
ctio
n
Tran
sfo
rmat
ion
Data Catalog
Fed
erat
ion
Dynamic QueryOptimization
Lifecycle Managemen
t
Data Caching
Categorize
QueryDiscover
Collaborate
Cost Based Optimizer
Metadata Management, Data Governance, Data Security, DevOps/Lifecycle Management
Unlimited Connectivity to Any Data TypeRelational DB’s: Oracle, DB2, Sybase, MS SQL Server, MySQL, PostgreSQL, Informix, MS Access…
Parallel DB’s & Appliances: Teradata, Netezza, Oracle Exadata, Sybase IQ, Greenplum, ParAccel…
Multidimensional OLAP Engines: SAP BW, MS SQL Server Analysis Services, Mondrian, Essbase…
SOAP / REST Web Services and Data Feeds: XML, RSS, ATOM, JSON, Odata, Delimited Files – CSV, log files, device feeds, ...
Enterprise Applications: SAP R3 / ECC, Oracle E-Business suite,, Siebel, PeopleSoft, SAS...
Content Management Sys (CMS): MS SharePoint, IBM FileNet, Documentum…
Modeling Tools: Erwin, Roshade, ER Studio…
MDM & Mapping: IBM Initiate, ontologies, taxonomies…
Mainframe / Legacy: Connectivity: Adabas, IMS, DB2, TN5250 / TN3270.
Plug-in architecture: third party Mainframe / Legacy Adapters...
Semantic repositories in Triple Stores / RDF: accesed via SPARQL endpoints
LDAP and Active Directory: as source data & security access
Big Data / NoSQL: Hadoop, Hive, HCatalog, Impala, Scoop, HBase, PIG, HDFS, MapReduce, AVRO, HDFS, Mongo DB, CouchDB, Neo4J, Cassandra, MarkLogic…
Cloud, SaaS: Salesforce, Google, Amazon, LinkedIn, Facebook, Twitter via APIs; Any Website, Form, any Web based Apps…
Enterprise Service Bus: JMS message queues, WebSphere MQ, Sonic, ActiveMQ…
Custom Connector SDK: access any application via API and procedural interfaces.
Semi-Structured Data: Web sites, Forms, applications, PDF, MS Word, MS Excel
Unstructured Data: websites, file systems, Email servers, databases, knowledgebase, indexes (Lucene, MS FAST, HP Autonomy…), RSS Feeds …
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Customer Centricity / MDM
✓ Complete View of Customer✓ Virtual MDM✓ Call Center Integration
Data Services
✓ Data as a Service✓ Data Marketplace✓ Data Services✓ Application and Data Migration
Cloud Solutions
✓ Cloud Modernization✓ Cloud Analytics✓ Hybrid Data Fabric
Data Governance
✓ GRC✓ GDPR✓ Data Privacy / Masking
BI and Analytics
✓ Self-Service Analytics✓ Logical Data Warehouse✓ Enterprise Data Fabric
Big Data
✓ Logical Data Lake✓ Data Warehouse Offloading✓ IoT Analytics
Denodo ‘Horizontal Solution’ Categories
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Reporting Tools & Applications via JDBC, ODBC and ADO.NET (examples below)
Also Via
▪ Web Services – SOAP, REST and OData
▪ JMS listeners for message queues (Active MQ, Sonic, IBM WebSphere MQ, etc.)
Denodo Information Self Service Web Tool
• Web based tool for exploration and discovery – Query, Search, Data lineage, Associations, Metadata, etc.
Consume data from any Application, BI Tool, W.S, ESB & Self Service
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z
Five Essential Benefits of Data Virtualization
1. Centralized access to Enterprise data
▪ Abstracts access to disparate data sources and complexities of location, format, protocol.
▪ Acts as a single virtual repository.
▪ Expose integrated business entity views and relationships between them.
2. Minimize replication and provides real-time information
▪ Leaves the data at its source; deliver only what is needed, on demand.
▪ Provisions data in real-time to consumers
▪ Diminishes the need for effort-intensive ETL processes.
3. Centralized security, metadata & governance
▪ Abstracts data source security models and enables unified security and governance.
▪ Extends unified governance across cloud and on-premises architectures
▪ Provides multiple forms of metadata (technical, business, operational) to facilitate
4. Tool agnostic
▪ Data in different formats and access paradigms tailored to different project needs
▪ JDBC/ODBC, SOAP, REST, OData, JMS
5. Self-service data services
▪ Enables creation of universal semantic models reflecting business taxonomy
▪ Connects data silos to provide best available information to drive business decisions
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ROI and TCO of Data Virtualization
Customer-reported projected savings by percentage
Data Integration Cost reduction
▪ 60-80% savings
Traditional Call Centres, Portals
▪ 30-70% savings
BI and Reporting
▪ 40-60% savings
ETL and Data Warehousing
▪ Project timelines of 6-12 months reduced to 3-6 months▪ Up to 65% reduction in time
“By 2018, organizations with data virtualization capabilities will spend 40% less on building and managing data integration processes for connecting distributed data assets.”
Gartner Predicts 2017: Data Distribution and Complexity Drive Information Infrastructure Modernization
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Gives DV its Highest Maturity Rating
19
“Means, DV can be
deployed with low risk and effort to
achieve maximum
value.”
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Denodo v7
Thanks!
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