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Debezium
Self-serve streaming data platform for building real-time ETL from DB, SaaS and filestores. Company behind Gazette and Estuary Flow OSS.
Open-Source project for streaming change data into (primarily) Apache Kafka.
n/a
Open-Source, or predictably priced pipelines at $0.50 / GB plus $0.14 / hr (~$100/mo) for any capture or materialization.
Open-Source. Typically requires 2+ full-time senior resources for production grade pipelines that require Kafka, Kafka Connect, Zookeeper, Debezium.
Open-source may or may not be cheaper all. With Debezium, you'll need to run the hardware and hire the team to support it.
Logical Decoding for Write-Ahead Log or Binlog enabled.
Logical Decoding for Write-Ahead Log or Binlog enabled, Kafka (usually), Kafka Connect, ZooKeeper.
Teams using Debezium should be highly proficient in Java to properly manage these packages.
MongoDB, MySQL,PostgreSQL, SQL Server, Salesforce, Firestore + 100 others sources and destinations.
MongoDB, MySQL, PostgreSQL, SQL Server, Oracle, DB2.
Debezium support limited to databases and no SaaS APIs. Estuary does not support Oracle/DB2 (coming Q4 2023)
Winter 2023
Yes
Debezium can be a good option where on-prem is required.
No resource management as Flow is fully managed.
Exactly-Once
At-least once semantics can create duplicates the destination, creating inaccurate results and excess cost.
Estuary manages partitioning of tables and communicates with replication slot. This avoids DB memory problems that would otherwise put a limit on uptake.
A connector handles 7K change events/second. Tables can be manually partitioned and multiple connectors created for more scalability. Issues can happen when replication slots fill during backfills.
For teams working with large tables, Debezium can be difficult to get working.
Automated schema evolution
Row-level data capture, but downstream destinations will have to be manually updated.
Automation will ensure that your destination always matches your source.
Data stored in a real-time data lake, backfilling is fully automated.
Manually triggered backfills to replay log from a point in time for a new consumer.
Automation can save you time and money.
Streaming SQL and javascript transforms with joins on both real-time and historical data. DBT as a destination.
Single-Message Transforms can perform basic transforms of a single message
With Debezium, it's necessary to do complex transforms in your destinaton or bring in a stream processing platform like Flink.
We're creating a new kind of DataOps platform thatempowers data teams to build real-time,data-intensive pipelines and applications, at scale,with minimal friction, in a UI or CLI. We aim to make real-time data accessible to the analyst, while bringing power tooling to the streaming enthusiast. Flow unifies a team's databases, pub/sub systems, and SaaS around their data, without requiring new investments in infrastructure or development.
Estuary develops in the open to produce both the runtime for our managed service and an ecosystem ofopen-source connectors. You can read more about our story here.