CLI
Using Apache Kafka and Confluent CLIs to produce and consume events
Apache Kafka® and Apache Flink® Tutorials
Learn stream processing the simple way. Use this cookbook of recipes to easily get started at any level.
Learn the basics
Build applications
Building event-driven applications with best practices like callbacks and exception handling
Kafka topics
Working with Apache Kafka topics and related configurations
Master advanced concepts
Flink SQL
Building event streaming applications with Flink SQL
- How to handle timestamps with Flink SQL
- How to count events having the same key with Flink SQL
- How to find the min or max field value by key with Flink SQL
- How to temporal join two streams with Flink SQL
- How to filter events with Flink SQL
- How to sort events with Flink SQL
- How to aggregate over tumbling windows with Flink SQL
- How to aggregate over hopping windows with Flink SQL
- How to aggregate over cumulating windows with Flink SQL
- How to split event streams into multiple derived streams with Flink SQL
- How to deduplicate events with Flink SQL
- How to deduplicate events per-time window with Flink SQL
- How to merge two streams into one with Flink SQL
- How to temporally join multiple streams of data with Flink SQL
- How to perform OVER Aggregations in Flink SQL
- How to fetch prior events by key with Flink SQL
- How to work with maps with Flink SQL
- How to expand arrays into rows per element with Flink SQL
- How to pattern match with Flink SQL's `MATCH_RECOGNIZE`
- How to get the top (or bottom) N events with Flink SQL
- How to get the top (or bottom) N events per-time window with Flink SQL
- How to create and deploy a Flink SQL UDF in Confluent Cloud
Flink Table API (Python)
Explore the Python Table API on Confluent Cloud for Apache Flink
Flink Table API (Java)
Explore the Java Table API on Confluent Cloud for Apache Flink
Route events
Deciding where to send events, splitting, merging, and filtering streams
Manipulate streams
Working with streams, tables, data formats, and other event-processing... operations
Aggregate data
Implementing stateful operations on events
Join data
Combining data from two or more sources based on common keys
Windowing operations
Putting stateful operations results into groups based on time
Connect data sources & sinks
Using APIs and connectors to produce data to Kafka topics or to write data out to external systems
Access fields
Reading fields within a record, and working with JSON
Manipulate Values
Implementing stateless operations on events
Testing, Monitoring, & Performance
How to test, monitor, and tune Kafka applications
- How to integration test a Kafka application with a native Kafka binary with Testcontainers
- How to centrally monitor Kafka clients via broker configuration
- How to use the Confluent Parallel Consumer library
- How to update partitions with ksqlDB
- How to handle uncaught exceptions in Kafka Streams
- How to count the number of messages in a Kafka topic with ksqlDB
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Ready to get started?
To try any of these out, make sure you first sign up for Confluent Cloud.