Eine Echtzeit-Brücke in die Cloud bauen – mit Confluent Platform 7.0 und Cluster Linking | Blog lesen

Level Up Your KSQL

Now that KSQL is available for production use as a part of the Confluent Platform, it has never been easier to run the open-source streaming SQL engine for Apache Kafka®. Which is not to say that everything is entirely obvious to the new user. A beginning or even intermediate streaming SQL user might still need a hand, and we’re here to give you one!

Maybe you’ve already been using KSQL, and you have fallen in love with its intuitive syntax for creating and enriching streams of real-time data. Maybe you run Confluent Platform, and you already love the handy KSQL user interface and Confluent Control Center’s stream monitoring capabilities to monitor the performance of your KSQL queries.

ksql-level-up

Or maybe not yet. Regardless, we can tell you that now is the time to level up your KSQL. Whether you are brand new to it 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. Stay tuned with us over the next few weeks as we release the Level Up Your KSQL video series that enables you to really understand KSQL.

 

 

There are more videos besides these. We also cover:

Interested in more? Learn more about what KSQL can do:

Did you like this blog post? Share it now

Subscribe to the Confluent blog

More Articles Like This

A Guide to Stream Processing and ksqlDB Fundamentals

Event streaming applications are a powerful way to react to events as they happen and to take advantage of data while it is fresh. However, they can be a challenge

How to Efficiently Subscribe to a SQL Query for Changes

Imagine that you have real-time data about what’s happening in the stock market, and you want to support a large number of customized dashboards displaying the data as it comes

Announcing ksqlDB 0.22.0

We’re pleased to announce ksqlDB 0.22.0! This release includes source streams and source tables as well as improved pull query (for key-range predicates) and push query performance. All of these