Examples: Transforming Data - Amazon Kinesis Data Analytics for SQL Applications Developer Guide
Preprocessing Streams with Lambda
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

For new projects, we recommend that you use the new Managed Service for Apache Flink Studio over Kinesis Data Analytics for SQL Applications. Managed Service for Apache Flink Studio combines ease of use with advanced analytical capabilities, enabling you to build sophisticated stream processing applications in minutes.

Examples: Transforming Data

There are times when your application code must preprocess incoming records before performing any analytics in Amazon Kinesis Data Analytics. This can happen for various reasons, such as when records don't conform to the supported record formats, resulting in unnormalized columns in the in-application input streams.

This section provides examples of how to use the available string functions to normalize data, how to extract information that you need from string columns, and so on. The section also points to date time functions that you might find useful.

Preprocessing Streams with Lambda

For information about preprocessing streams with Amazon Lambda, see Preprocessing Data Using a Lambda Function.

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