Examples: Transforming Data - Amazon Kinesis Data Analytics for SQL Applications Developer Guide
Preprocessing Streams with Lambda

After careful consideration, we have decided to discontinue Amazon Kinesis Data Analytics for SQL applications in two steps:

1. From October 15, 2025, you will not be able to create new Kinesis Data Analytics for SQL applications.

2. We will delete your applications starting January 27, 2026. You will not be able to start or operate your Amazon Kinesis Data Analytics for SQL applications. Support will no longer be available for Amazon Kinesis Data Analytics for SQL from that time. For more information, see Amazon Kinesis Data Analytics for SQL Applications discontinuation.

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 AWS Lambda, see Preprocessing Data Using a Lambda Function.

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