Watch live presentation here: https://goo.gl/UcZEHU Big data projects are becoming mature and consistent. However, they remain siloed compared to the enterprise data. In addition, now new streaming data needs to integrated as well. Watch this Denodo DataFest 2017 session to discover: • How big data projects can be combined with other enterprise data. • How to integrate streaming data into the mix. • Benefits of aggregating the data without having to move them into a centralized repository.

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Integrating Big Data and Streaming
Data with Enterprise Data
October 16, 2017

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approval.
Capital
Aviation
Power
Healthcare Oil & Gas
Transportation
Lighting
Global Ops
Digital
Additive
Renewables
Multiple Mighty Businesses

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approval.
Machines, Chips, Sensors & Data everywhere

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approval.
Need for Data Integration & Data Pipe Lines
October 16, 2017Presentation Title 4

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approval.
Integrating Streaming Data with Enterprise Data
Integration needs will depend on the use cases

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approval.
Real-Time Data & Data Pipeline
Data orchestration strategies will vary depending on use cases

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approval.
A Look at few Data Collection &
Processing Tools
Akka
Kafka
Spark Streaming
October 16, 2017Presentation Title 7

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approval.
Akka
Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven
applications for Java and Scala. Uses reactive streaming model by leveraging back-pressure
controlled messages.
Reactive Streams – Pull based back Pressure

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approval.
Kafka
Kafka is a distributed publish-subscribe messaging system that is designed to be fast,
scalable, and durable. Like many publish-subscribe messaging systems, Kafka maintains feeds of
messages in topics. Producers write data to topics and consumers read from topics. Since Kafka is
a distributed system, topics are partitioned and replicated across multiple nodes.

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approval.
Spark Streaming
Spark Streaming is an extension of the core Spark API that enables scalable, high-
throughput, fault-tolerant stream processing of live data streams.

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approval.
Multiple Data Integration Techniques
What option would we choose?
October 16, 2017Presentation Title 11

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approval.
Data Integration using DV
A virtual data stitching can be enabled to integrate data from streaming data
and enterprise datasets regardless of data velocity, variety and volume.
Data
Warehouse(s)
&
Data Mart(s)
Spark
Streamin
g
Data
Analytics
SparkSQ
L
Data Lake (s)
NoSQL
Database(s)
Data Virtualization layer to
connect big data based
data sources

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approval.
If you have these scenarios…
 Streaming datasets
 Disparate data sources within enterprise
 Structured or Unstructured datasets
 Need for stitching historical and new datasets
 Real-Time Analytical solutions
 Need for an agile solution