Open-source Striim alternatives

Apache Kafka is a platform for distributed event streaming. Teams use it to move and process streams of events for data pipelines, streaming analytics, data integration and business-critical applications. The code lives in the Apache Software Foundation repository, is written mainly in Java with Scala for parts of the broker, and is released under the Apache-2.0 license.

Kafka is installed software that you run yourself. The project builds and tests against recent Java versions, supports Scala 2.13 only, and documents how to build a JAR and start a broker through its quickstart. The repository also contains client and streams modules, along with test tooling for unit and integration runs. Because Kafka is operated as infrastructure, adopters typically run clusters on their own servers or on Kubernetes, though managed services from other vendors exist.

Debezium is an open-source platform for change data capture. You configure it to monitor your databases, and your applications then consume an event for each row-level change. Only committed changes are visible, so consumers do not need to deal with transactions that are rolled back.

It provides one consistent model for change events across different database systems, so applications are insulated from the details of each engine. Changes are recorded in durable, replicated logs, which means a consumer can be stopped and restarted and still receive every event it missed. This removes the need for database-specific triggers or ad hoc change-monitoring code, which the project describes as hard to get right when ordering and low impact on the database both matter.

Debezium is built with Java and Kafka Connect in mind, as the project topics show, and is licensed under Apache-2.0, with the ANTLR grammars in its DDL parser module under the MIT licence. It runs as part of your own data infrastructure and suits data engineers building event streaming pipelines, cache invalidation or data replication.

RisingWave is an event streaming platform, positioned for agentic AI and real-time applications, that continuously takes in data, transforms it and serves it. It is meant to replace the familiar chain of Debezium (change data capture), Kafka (transport), Flink (processing) and a serving database with one system, removing the latency and operational work of each hop.

Sources include webhooks from SaaS applications, native change data capture from PostgreSQL and MySQL, event streams such as Kafka, Pulsar and Kinesis, and batch data from S3 and warehouses, all unified under a SQL interface where streams and tables can be joined. Processing is incremental: if upstream data changes, just the affected results get recomputed, which keeps materialized views up to date without full recomputation. Results are queryable directly, and the topics mention PostgreSQL compatibility and Apache Iceberg.

RisingWave is written in Rust and licensed under Apache-2.0. It can be tried with Docker or Kubernetes using the quick start guide, and it suits data engineers who want streaming pipelines defined in SQL instead of several separate services.

Duckle is an open-source ETL and ELT platform for teams that want their data pipelines running on their own infrastructure. You build pipelines on a visual canvas, in Python or in SQL, then ship the same file to your own server or cloud account, where a headless runner executes it on a schedule.

Pipelines compile to SQL on DuckDB and use all the cores available, so a larger machine runs them faster. The platform lists support for a large catalog of components, dbt, change data capture, data quality checks, reverse ETL and lineage, along with a web console, roles, an audit trail and an MCP server so AI agents such as Claude or Cursor can work with it. Each pipeline is a single file that can live in git.

Duckle is written in Rust and licensed under Apache-2.0, and it runs headless in Docker or on a plain server, with Kubernetes among its topics. It states there is no vendor cloud and no per-row billing. It is an independent project by SlothFlowLabs and is not affiliated with DuckDB Labs or MotherDuck. It suits data engineers who want a self-hosted alternative to managed pipeline services.

Dozer is a real-time data movement platform that uses change data capture (CDC) to stream data continuously from source databases into destinations such as data warehouses. It is aimed at data engineering teams who need faster, simpler CDC pipelines than assembling Debezium and Kafka themselves, with native support for stateless transformations along the way.

It supports a range of source connectors including Postgres, MySQL, Snowflake, Kafka, MongoDB, Amazon S3, Google Cloud Storage, Oracle and Aerospike, with extraction and resuming support varying by connector and some sources reserved for the enterprise edition. On the sink side, it supports ClickHouse, Postgres, MySQL and BigQuery in the open-source edition, with Oracle and Aerospike sinks reserved for enterprise. The project notes its own typical use is moving data into ClickHouse to build data APIs and integrations with LLMs.

Dozer is configured through a single configuration file and is written in Rust for performance, claiming to be significantly faster than a Debezium-plus-Kafka setup for comparable CDC workloads. It is released under the AGPL-3.0 license, with full documentation available for setup and configuration details.