Getting Started with Amazon Kinesis Data Analytics for Apache Flink for Python
This section introduces you to the fundamental concepts of a Kinesis Data Analytics for Apache Flink using Python and the Table API. It describes the available options for creating and testing your applications. It also provides instructions for installing the necessary tools to complete the tutorials in this guide and to create your first application.
Topics
Getting started with Pyflink - The Python Interpreter for Apache | Amazon Web Services
Before you begin, we encourage you to watch the following video:
Components of Kinesis Data Analytics for Flink Application
To process data, your Kinesis Data Analytics application uses a Python application that processes input and produces output using the Apache Flink runtime.
Kinesis Data Analytics application has the following components:
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Runtime properties: You can use runtime properties to configure your application without recompiling your application code.
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Table Source: The application consumes data by using a source. A source connector reads data from a Kinesis data stream, an Amazon MSK topic, or similar. For more information, see Table API Sources.
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Functions: The application processes data by using one or more functions. A function can transform, enrich, or aggregate data.
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Sink: The application produces data to external sources by using sinks. A sink connector writes data to a Kinesis data stream, a Kinesis Data Firehose delivery stream, an Amazon MSK topic, an Amazon S3 bucket, and so on. For more information, see Table API Sinks.
After you create and package your application code, you upload the code package to an Amazon S3 bucket. You then create a Kinesis Data Analytics application. You pass in the code package location, a streaming data source, and typically a streaming or file location that receives the application's processed data.
Prerequisites
Before starting this tutorial, complete the first two steps of the Getting Started with Amazon Kinesis Data Analytics for Apache Flink (DataStream API):
To get started, see Create an Application.