UNITING INTERNET OF THINGS

AND BIG DATA

YOU CAN’T DO THE “THINGS” OF IoT IN A TRADITIONAL DATA WAREHOUSE ENVIRONMENT

IoT is defined by many to be the “collection, processing, and analysis of information from sensors on a large number of disparate devices.”

The Internet of Things aligns well with big data initiatives because they share the same core attributes: Data collection from multistructured data sources Data management at a large scale Complex processing

NEARLY 50% OF RESPONDENTS STATED INTERNET OF THINGS IS

ESSENTIAL OR IMPORTANT TO THEIR ORGANIZATIONAL STRATEGIES

Adopted and an essential part of our business

Adopted and an important part of our business

Adopted and mostly supplemental to other

types of computing

Planned for adoption in the near future (3-6 months)

Being reasearched in the next year (6-12 months)

Not planned for research or adoption

Percentage of Respondents

THESE PROJECTS HAVE A LOW-LATENCY REQUIREMENT

IN PROCESSING

TO MEET THOSE IoT OBJECTIVES...

Real-time/near real-time

processing of data

Intra-hour processing of

data

Intra-day processing

of data

Next day processing

Processing is relatively

time-insensitive

Workload LatencyIntra-day

Near real-time

Time insensitive

32.4% 23.3%

19.8%

13.9%10.5%

Real-time streaming data integration

Change data capture (CDC) (e.g., tracking and executing on changes made to enterprise)

Multipoint data services layer (e.g., data visualization, data federation)

Extract-load-transform processing in data store/database (ELT)

Extract-transform-load processing external to data store/database (ETL)

Data replication (e.g., standardized duplication of enterprise data sources)

Batch, bulk data integration

Native operating system or database utilities

Point-to-point integration bus

...STANDARD ETL WON’T WORK FOR THE WORLD OF IoT. WE NEED TO MOVE BEYOND BATCH TO

MORE STREAMING TECHNOLOGIES BASED ON OPERATIONAL PROCESSES AND THE

APPLICATIONS THAT DRIVE THOSE “THINGS”

12.1%

11.5%

11.2%

10.8%

10.0%

12.0%

11.2%

10.8%

10.0%

MOST POPULAR BIG DATA PROJECT STRATEGY IS TO USE CONFIGURABLE APPLICATIONS TO

IMPLEMENT BIG DATA PROJECTS

Customizable applications from external provider

Hand-coded development by internal resource(s)

Custom development by

external consultant(s)

Productized development by external provider

Stand-alone products from external provider(s)

Bespoke development by internal

staff contractor(s)

20.7%

17.7%16.3%

15.7%

15.0%14.3%

Project ImplementationStrategy

Configurable Products

Manual Development

SNAPLOGIC EMPOWERS ORGANIZATIONS TO INTEGRATE BIG DATA AND STREAMING

INTERNET OF THINGS APPLICATIONS

HIGHLY CONFIGURABLE WITHOUT TECHNICAL

RESOURCES

HANDLES STREAMING AND BATCH DATA

INTEGRATE DATA FROM MULTIPLE SOURCES,

INCLUDING MQTT AND OTHER IOT PROTOCOLS

UnifiedPlatform

Connects data,applications, APIs,and IoT devices

faster

Self-servicefor “citizenintegrators”

and developers

In 2014, EMA and 9sight Consulting surveyed 351 diverse business and technology professionals to provide their insights on big datastrategies and implementation practices, including Internet of Things strategies and implementations. The 2014 survey instrument wasexecuted in December 2014.

Hybrid platformbuilt for

elastic scale

300+ intelligentapplication anddata connectors

Drag-and-dropSnapReducepipelines andHadooplex big

data processing

ProductiveUX

ModernArchitecture

ComprehensiveConnectivity

Hadoopfor Humans

SNAPLOGIC PROVIDES . . .

17.1%

29.3%

16.8%

10.5%

10.5%

15.7%

TO LEARN MORE, VISIT

www.SnapLogic.com