Python-native
Pure Python, meaning no wrappers around Java and no cross-language debugging.
Sources & Sinks API for building custom connectors that integrate data with Kafka whilst handling retries and backpressure. JSON, Avro, Protobuf and schema registry support to keep your ever-changing data valid and clean.
Work with streaming data like it's in a database
Treat real-time data streams as continuously updating tables via a Streaming DataFrame API. Ideal for transitioning projects from Pandas or PySpark.
Use built-in operators for aggregation, windowing, filtering, group by, branching, merging and more to build stateful applications in fewer lines of code.
Flexible, scalable and fault tolerant
Clever checkpointing and exactly-once processing guarantees ensure your data pipelines are durable and fault tolerant through unpredictable infrastructure issues.Â
By leveraging Kafka, you’re building infinitely horizontally scalable services. Data replication ensures redundancy and high availability for your data consumers.