Decodable is a managed, serverless platform for real-time ETL, ELT, and stream processing, built on Apache Flink and Debezium. Teams can create pipelines with SQL, Java, or Python and manage them through the web UI, CLI, APIs, or dbt. SQL supports transformations, filtering, stateful joins, and complex event processing. Pipelines process data exactly once by default and can resume from their prior point after a stop or failure. Connectors include Apache Kafka, PostgreSQL, Apache Iceberg, and Snowflake, with Debezium-powered change data capture. Workloads can run on Decodable-hosted infrastructure or through a Bring Your Own Cloud deployment. The free plan does not expire and requires no credit card; it includes 24 hours or 10GiB of stream retention, up to 20 streams, and four running tasks. On Demand is 0.12 USD per month, billed monthly, while Enterprise is 0.10 USD per year, billed annually with committed capacity. Decodable describes its users as data engineers, application developers, and data scientists building real-time applications and services.
Who it is for
It suits data engineers, application developers, and data scientists building real-time applications or services. Teams that need managed streaming pipelines and a choice between hosted infrastructure and Bring Your Own Cloud may find it relevant.
What is good
- Build pipelines with SQL, Java, or Python.
- Exactly-once processing is the default.
- Pipelines can resume after a stop or failure.
- Free plan does not expire or require a credit card.
What to know first
- Free retention is limited to 24 hours or 10GiB.
- Free plan allows up to 20 streams and four running tasks.
- Free support is best-effort.
Verdict
Decodable offers real-time pipeline development with multiple languages, management interfaces, and deployment choices. Its free plan has defined retention and capacity limits, while paid plan prices are listed with different billing terms.
Decodable plans and pricing
All plansCompared on change data capture software
- Free plan
- Yes
- Deployment model
- hybrid
- Capture method
- log-based
- Schema evolution
- No
- Initial snapshot
- Yes
