Recent data from Enterprise Management Associates and 9sight Consulting surveyed 351 diverse business and technology professionals to provide their insights on big data strategies and implementation practices, including Internet of Things strategies and implementations. To learn more, visit: www.snaplogic.com/big-data

1. 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 Latency
Intra-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
Implementation
Strategy
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
Unified
Platform
Connects data,
applications, APIs,
and IoT devices
faster
Self-service
for “citizen
integrators”
and developers
In 2014, EMA and 9sight Consulting surveyed 351 diverse business and technology professionals to provide their insights on big data
strategies and implementation practices, including Internet of Things strategies and implementations. The 2014 survey instrument was
executed in December 2014.
Hybrid platform
built for
elastic scale
300+ intelligent
application and
data connectors
Drag-and-drop
SnapReduce
pipelines and
Hadooplex big
data processing
Productive
UX
Modern
Architecture
Comprehensive
Connectivity
Hadoop
for Humans
SNAPLOGIC PROVIDES . . .
17.1%
29.3%
16.8%
10.5%
10.5%
15.7%
TO LEARN MORE, VISIT
www.SnapLogic.com