CLI
Using Apache Kafka and Confluent CLIs to produce and consume events
Apache Kafka® 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
- Aggregation - counting
- Aggregation - min max
- Temporal joins
- Filtering events
- Tumbling windows
- Hopping windows
- Cumulating windows
- Splitting event streams
- Deduplication
- How to filter duplicate events from a Kafka topic with Flink SQL
- How to merge two streams into one with Flink SQL
- How to perform OVER Aggregations in 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
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 connectors to read data to Kafka topics and to write data out
- Connect to a PostgreSQL source (JDBC)
- Connect to a MQTT source
- Connect to a Snowflake sink
- Connect to an Elasticsearch Service sink
- Convert timezone before sending to a database
- Build and run a custom connector on Confluent Cloud
- How to configure Confluent Cloud to connect to an AWS S3 bucket with PrivateLink
Access fields
Reading fields within a record, and working with JSON
Manipulate Values
Implementing stateless operations on events
Explore top use cases
Anomaly and pattern detection
Identify patterns and unusual activity to detect fraud and monitor network health
Customer 360
Combine data sources for a unified 360-degree view of the customer across channels
Cybersecurity
Protect critical systems and sensitive information by detecting threats in real-time
Predictive analytics
Train predictive models by using machine learning to anticipate future outcomes
Real-time analytics
Extract real-time insights by performing analytics on streaming data as it’s generated
Logistics and IoT Management
Optimize supply chain operations and detect future failures with predictive maintenance
Real-time Alerts and Notifications
Build real-time alerts and notifications to power digital customer experiences
Infrastructure Modernization
Modernize legacy technologies and rationalize infrastructure footprint with modern systems
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Ready to get started?
To try any of these out, make sure you first sign up for Confluent Cloud and provision a ksqlDB application.