The document outlines the topics that will be covered in an Apache Flink online training, including: what Apache Flink is; why use Apache Flink; its architecture, features, and deployment; its streaming, batch processing, and table APIs; complex event processing; graph processing; and integration with Hadoop. The training will cover Apache Flink's stream processing engine, fault tolerance, state management, and support for stream, batch, and iterative processing using its dataflow model.

1. APACHE FLINK

2. Apache Flink
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The following topics will be covered in our
Apache Flink Online Training:

3. What is Apache Flink?
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Apache Flink is an open source stream processing framework developed by
the Apache Software Foundation. The core of Apache Flink is a distributed
streaming dataflow engine written in Java and Scala. Apache Flink’s dataflow
programming model provides event-at-a-time processing on both finite and
infinite datasets. At a basic level, Flink programs consist of streams and
transformations.

4. ….. Continues
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Conceptually, a stream is a (potentially never-ending) flow of data records, and
a transformation is an operation that takes one or more streams as input, and
produces one or more output streams as a result. Programs can be written in
Java, Scala, Python, and SQL and are automatically compiled and optimized into
dataflow programs that are executed in a cluster or cloud environment.

5. Why Apache Flink?
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• Flink provides a high-throughput, low-latency streaming engine as well as
support for event-time processing and state management. Flink applications
are fault-tolerant in the event of machine failure and support exactly-once
semantics. Flink executes arbitrary dataflow programs in a data-parallel and
pipelined manner. Flink’s pipelined runtime system enables the execution of
bulk/batch and stream processing programs. Furthermore, Flink’s runtime
supports the execution of iterative algorithms natively.

6. Flink Introduction
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• Architecture
• Distributed Execution
• Job Manager
• Task Manager
• Features
• Deploying Flink on Google Cloud and AWS

7. Data Stream API
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• Execution environment
• Data sources
• Transformations
• Data sinks
• Connectors

8. Batch Processing API
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• Data sources
• Transformations
• Broadcast Variable
• Connectors to various Systems
• Iterations

9. Structure data handling using Table API
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• Registering tables
• Accessing the registered table
• Operators
• Data types
• SQL

10. Complex event processing
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• Introduction to CEP and Flink CEP
• Event Streams
• Pattern API
• Continuity
• Selecting from Pattern

11. Graph API
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• Flink Graph Library – Gelly
• Graph Representation
• Graph Properties
• Graph Transformations
• Graph Mutations
• Iterative Graph Processing
• Scatter-Gather Processing

12. Integration between Flink and Hadoop
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• Flink-Yarn Session
• Job Submission to Flink
• Execution of a Flink job on YARN
• Flink and YARN interaction details

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