From Batch to Real-time: How to Get Started with Stream Processing
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Organizations are looking to modernize their data infrastructure at an increasing rate, by moving from batch processing to real-time streaming data pipelines with Kafka. A key component to this transition is stream processing, which enables you to process, enrich, and query your data without intermediate batch ETL jobs.
ksqlDB makes stream processing easy by providing you with a simple SQL syntax for doing anything you need with data in motion. Best of all, ksqlDB comes fully managed as part of our cloud-native Kafka service, Confluent Cloud. Way more than Kafka, Confluent Cloud provides all the tools needed to bring together real-time streams of data to connect the whole business giving teams the data they need, when they need it.
Interested in bringing stream processing to your organization, but unclear on how to get started? Designed to help you go from idea to proof of concept, this online talk dives into a few of the most popular stream processing use cases and workloads to help get you up and running with ksqlDB.
Register now to learn how you can bring stream processing to your organization by understanding:
- Key use cases to get started with ksqlDB, including popular examples to introduce its unique stream processing capabilities to your organization
- How to optimize your capacity planning with the various CSU options – 1, 2, 4, 8, or 12
- Best practices for migrating to a stream processing architecture
