Amazon Kinesis Data Analytics for SQL Features

Amazon Kinesis Data Analytics for SQL

Overview

After careful consideration, we have made the decision to discontinue Amazon Kinesis Data Analytics for SQL applications effective January 27, 2026. We recommend that you use Amazon Managed Service for Apache Flink Studio or Amazon Managed Service for Apache Flink. Amazon Managed Service for Apache Flink Studio combines ease of use with advanced analytical capabilities, enabling you to build stream processing applications in minutes. You can find code and architecture examples to help you move your Kinesis Data Analytics for SQL workloads to Amazon Managed Service for Apache Flink Studio in our documentation.

Amazon Kinesis Data Analytics for SQL Features

Support for Standard SQL

Kinesis Data Analytics supports standard ANSI SQL.

Integrated Input and Output

Kinesis Data Analytics integrates with Amazon Kinesis Data Streams . Just point Kinesis Data Analytics at the input stream and it will automatically read the data, parse it, and make it available for processing. You can emit processed results to other AWS services including Amazon S3 , Amazon Redshift , and Amazon OpenSearch Service through Kinesis Data Firehose. You can also send output data to Amazon Kinesis Data Streams to build advanced stream processing pipelines.

Console-based SQL Editor

Use a console-based editor to build SQL queries using streaming data operations like sliding time-window averages. You can also view streaming results and errors using live data to debug or further refine your script interactively.

Easy-to-Use Schema Editor

Kinesis Data Analytics provides an easy-to-use schema editor to discover and edit input data structure. The wizard automatically recognizes standard data formats such as JSON and CSV. It infers the structure of the input data to create a baseline schema, which you can further refine using the schema editor.

Pre-built SQL Templates

The interactive SQL editor comes bundled with a collection of SQL templates providing baseline SQL code for the most common types of operations such as aggregation, per-event transformation, and filtering. You simply select the template appropriate for your analytics task and then edit the provided code using the SQL editor to customize it for your specific use case.