Skip the streaming platform tax
Kafka tuning, connector babysitting, consumer-lag debugging, and rebalancing storms are ours to own. Your engineers go back to shipping product.
Artie vs. Debezium
Debezium captures change events. Artie captures changes, runs the streaming platform, handles schema drift, and stays accountable when something breaks at 2am.
Open-source CDC is powerful. Operating it isn't free.
| Factor | Debezium (DIY stack) | Artie |
|---|---|---|
| Built for | Open-source DIY CDC framework | Fully managed real-time database replication |
| Architecture | Debezium connectors + Kafka + Schema Registry + sink connectors Often paired with Flink or Kafka Streams for transforms | Durable streaming, managed end-to-end (Kafka-backed under the hood) |
| Operational ownership | Your team operates Kafka, connectors, schema registry, and sinks | Fully managed by Artie |
| Time to first pipeline | Weeks to months | Minutes |
| Replication latency | Seconds, with tuning of partitions, batch sizes, and sink flush intervals | Seconds, out of the box |
| Delivery semantics | At-least-once by default Exactly-once requires significant engineering | Exactly-once |
| Schema evolution | Emits DDL events; downstream merge logic is yours | Auto-applies DDL, deletes, and type changes |
| Backfill behavior | DIY (initial snapshot + custom incremental backfill logic) | Free, online, parallel with CDC, replica-aware |
| Failure recovery | Build your own replay and dedupe; runbooks are yours | Kafka offset replay, exactly-once delivery |
| Impact on source DB | Configure replica routing yourself | Reads from replica by default; zero impact on production traffic |
| Sharded / multi-tenant fan-in | DIY routing across Kafka topics | Many-to-one fan-in 1,000s of shards → unified schema |
| Observability | Stitched together: Kafka metrics, connector logs, Flink job health, custom dashboards | Per-table lag, throughput, and alerting via Datadog/PagerDuty |
| PII controls | Custom Single Message Transforms (SMTs) you maintain | Column include/exclude/hashing on all plans |
| Enterprise compliance | You own SOC 2 / HIPAA evidence for your stack | SOC 2 Type II, HIPAA |
| Deployment options | Self-managed anywhere | Cloud or BYOC (your VPC) |
| Total cost shape | Low vendor spend, high engineer-hours and opportunity cost | Predictable contracted volume tier |
| Support / SLA | Community + paid Confluent or Aiven for the Kafka piece No single point of accountability | Vendor-backed SLA, single point of accountability |
Where Artie wins
Schema evolution that just works
Add a column, drop a field, change a type. Artie auto-propagates DDL, deletes, and type changes downstream. No firefighting, no manual refreshes.
Exactly-once, with a recovery playbook
Durable streaming with offset replay and exactly-once delivery. When something goes sideways at 2am, our on-call answers – not yours.
Run it where your data lives
Artie Cloud or BYOC in your VPC (Enterprise) – same product, same UX. SOC 2 Type II and column-level PII controls (include, exclude, hash) on every plan. HIPAA-ready for regulated industries. Keep the control DIY gave you and lose the maintenance.
Switching from Debezium to Artie takes hours, not weeks
What stays the same
- Your sources: Postgres, MySQL, MongoDB, and more
- Your destinations: Snowflake, BigQuery, Redshift, Databricks, Iceberg
- Your downstream stack: transformations, dashboards, BI tools keep reading the same tables
- Your existing Kafka cluster: keep it running for non-CDC workloads if you want
The cutover playbook
- Run Artie in parallel with Debezium during a validation period. Both write to the same warehouse without conflict.
- Compare row counts, latency, and correctness side by side.
- Repoint downstream consumers to read from Artie tables.
- Decommission the Debezium connectors (and Kafka topics, if no longer needed).
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