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Data Integration Architecture
For Real-time Data and IoT
Author: Hari Nadathur
Integration Platform
Traditional Application Integration
● Integration Platforms
composed of multiple
layers of abstraction
handling technical
capabilities, e.g. XML,
JSON payload formats
● Emphasis on successful
delivery of individual
business transaction from
source to destination
application, e.g. Sales
Order
● Business Intelligence is the
layer addressed at the very
end of the abstraction, and
is an afterthought
Source
Application
Data
Enrichment
Communication
Protocol
Payload format
Guaranteed
Delivery
Routing
Business
Intelligence
Destination
Application
Processing at the
Edge
Processing in the
Cloud
Data Integration for the Internet of Things
● High volume streams of
events are processed at
the edge, micro-patterns
identified
● Higher order processing
performed in the cloud to
identify business insights
● Emphasis not on
individual events but
streams
● Architecture should allow
edge computing and
scaling to extreme
volumes
Higher Order
Processing
Identify micro-
patterns
Devices
Actionable
Insights/
Business
Intelligence
Identify micro-
patterns
Sample Integration
Devices
Processing at the
edge
Apache Flink/
AWS Lambda
Processing in the
Cloud
Apache
Kafka
Apache
Spark
Apache Flink/
AWS Lambda
● Apache Flink handles
high volume event
streams, identifying
micro-patterns
● Apache Kafka stores
these patterns and can
scale to massive volumes
of data
● Apache Spark provides
business insights from
these stored micro-
patterns

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Data Integration Architecture for Real-time data and IoT

  • 1. Data Integration Architecture For Real-time Data and IoT Author: Hari Nadathur
  • 2. Integration Platform Traditional Application Integration ● Integration Platforms composed of multiple layers of abstraction handling technical capabilities, e.g. XML, JSON payload formats ● Emphasis on successful delivery of individual business transaction from source to destination application, e.g. Sales Order ● Business Intelligence is the layer addressed at the very end of the abstraction, and is an afterthought Source Application Data Enrichment Communication Protocol Payload format Guaranteed Delivery Routing Business Intelligence Destination Application
  • 3. Processing at the Edge Processing in the Cloud Data Integration for the Internet of Things ● High volume streams of events are processed at the edge, micro-patterns identified ● Higher order processing performed in the cloud to identify business insights ● Emphasis not on individual events but streams ● Architecture should allow edge computing and scaling to extreme volumes Higher Order Processing Identify micro- patterns Devices Actionable Insights/ Business Intelligence Identify micro- patterns
  • 4. Sample Integration Devices Processing at the edge Apache Flink/ AWS Lambda Processing in the Cloud Apache Kafka Apache Spark Apache Flink/ AWS Lambda ● Apache Flink handles high volume event streams, identifying micro-patterns ● Apache Kafka stores these patterns and can scale to massive volumes of data ● Apache Spark provides business insights from these stored micro- patterns