KSQL is the open source, streaming SQL engine that enables real-time data processing against Apache Kafka®.
KSQL makes it easy to read, write, and process streaming data in real-time, at scale, using SQL-like semantics. It offers an easy way to express streaming transformations.
KSQL provides powerful stream processing capabilities such as joins, aggregations, event-time windowing, all while leveraging the fully supported, enterprise-ready Confluent Platform.
Learn how to build real-time streaming applications with KSQL. This talk explains the KSQL engine architecture, and how to design and deploy interactive, continuous queries for streaming ETL and real-time analytics.Watch Video
Whether you are brand new to KSQL or ready to take it to production, now you can dive deep on core KSQL concepts, streams and tables, enriching unbounded data and data aggregations, scalability and security configurations, and more.
Get an introduction to the concept of stream processing with Apache Kafka and KSQL.Watch Now
Learn about relating KSQL to clients, choosing the right API and how KSQL uses Kafka topics.Watch Now
KSQL use cases include data exploration, arbitrary filtering, streaming ETL and more.Watch Now
Find out how to get KSQL, start the KSQL server and CLI, along with other syntax basics.Watch Now
Distinguish a STREAM from a TABLE, and discover how streaming queries are unbounded.Watch Video
Explore Kafka topic data. Create a STREAM or TABLE. Identify fields, metadata and formats.Watch Video
Stream queries, read topics, discover persistent and non-persistent queries and more.Watch Video
Use scalar functions, change field types, filter and merge data and rekey streams with KSQL.Watch Video
Review various aggregate functions (e.g., MAX, MIN), windowing and late-arriving data.Watch Video
Build a streaming ETL pipeline, scale processing, secure KSQL and monitor KSQL performance.Watch Now
Apache Kafka is a popular choice for powering data pipelines. KSQL makes it simple to transform data within the pipeline, readying messages to cleanly land in another system.
CREATE STREAM vip_actions AS
SELECT userid, page, action FROM clickstream c LEFT JOIN users u ON c.userid = u.user_id
WHERE u.level = 'Platinum';
KSQL is a good fit for identifying patterns or anomalies on real-time data. By processing the stream as data arrives you can identify and properly surface out of the ordinary events with millisecond latency.
CREATE TABLE possible_fraud AS
SELECT card_number, count(*)
WINDOW TUMBLING (SIZE 5 SECONDS)
GROUP BY card_number
HAVING count(*) > 3;
Kafka’s ability to provide scalable ordered messages with stream processing make it a common solution for log data monitoring and alerting. KSQL lends a familiar syntax for tracking, understanding, and managing alerts.
CREATE TABLE error_counts AS
SELECT error_code, count(*) FROM monitoring_stream WINDOW TUMBLING (SIZE 1 MINUTE) WHERE type = 'ERROR' GROUP BY error_code;