> ## Documentation Index
> Fetch the complete documentation index at: https://docs.planasonix.com/llms.txt
> Use this file to discover all available pages before exploring further.

# CDC sources

> Capture database changes in real-time with change data capture.

Change data capture (CDC) sources read inserts, updates, and deletes from operational databases or their transaction logs and emit them as a stream Planasonix can process. You choose capture mode, initial snapshot behavior, and tables to include, then connect downstream transforms and destinations.

## Database CDC

Planasonix supports CDC for common relational engines. Exact prerequisites (binary logging, supplemental logging, permissions) appear in each connector’s setup wizard.

<AccordionGroup>
  <Accordion title="PostgreSQL">
    Use logical decoding (for example, `pgoutput` or plugin-based slots) with replication permissions. Monitor replication slot lag on the database to avoid disk growth from idle consumers.
  </Accordion>

  <Accordion title="MySQL">
    Enable row-based binary logging and grant replication client privileges. Large wide tables benefit from careful primary key design to keep event size predictable.
  </Accordion>

  <Accordion title="SQL Server">
    Use change tracking or CDC features as supported by the connector; ensure agents can read from the capture instance without blocking OLTP workloads.
  </Accordion>

  <Accordion title="Oracle">
    Configure LogMiner or vendor-recommended equivalents per connector docs; archive log retention must exceed your worst-case consumer downtime.
  </Accordion>

  <Accordion title="MongoDB">
    Capture change streams on collections you authorize; consider resume tokens and schema evolution when documents are polymorphic.
  </Accordion>
</AccordionGroup>

Always test **initial snapshots** on a replica before pointing at production primaries if your DBA policy requires it.

## Event buses and cloud streaming

In addition to database CDC, you can ingest from high-throughput buses and managed streaming services:

| Platform             | Typical use                                                  |
| -------------------- | ------------------------------------------------------------ |
| Apache Kafka         | Durable topics from microservices or CDC Debezium connectors |
| Amazon Kinesis       | AWS-native streaming with shard-based scaling                |
| Apache Pulsar        | Multi-tenant messaging with geo replication                  |
| Google Cloud Pub/Sub | Asynchronous work queues and GCP-native events               |
| Azure Event Hubs     | Kafka-compatible endpoints on Azure                          |
| NATS                 | Lightweight messaging for edge and internal services         |
| Vendor webhooks      | HTTPS push endpoints normalized into an internal stream      |

<Tabs>
  <Tab title="Kafka and compatible">
    Configure bootstrap servers, SASL/SSL, consumer groups, and topic patterns. Set partition-aware parallelism only when your downstream processing is associative per key.
  </Tab>

  <Tab title="Cloud-managed">
    Use IAM or cloud-specific auth as guided by the connection wizard; rotate keys on the schedule your security team defines.
  </Tab>

  <Tab title="Webhooks">
    Expose a secured HTTPS ingress that validates signatures, then treat the payload as a stream of events with schema validation early in the graph.
  </Tab>
</Tabs>

## Operational checklist

<Steps>
  <Step title="Rightsize retention">
    Ensure broker and database retention cover maintenance windows plus worst-case recovery time.
  </Step>

  <Step title="Schema strategy">
    Define how additive schema changes propagate; use parsers and [data contracts](/governance/data-contracts) where you enforce compatibility.
  </Step>

  <Step title="Backpressure">
    Monitor consumer lag; scale processors or destinations before lag triggers SLA breaches.
  </Step>
</Steps>

## Related topics

<CardGroup cols={2}>
  <Card title="Stream processing" icon="water" href="/streaming/stream-processing">
    Windows and transforms on live data.
  </Card>

  <Card title="Observability" icon="gauge-high" href="/observability/overview">
    Metrics and alerts for streaming health.
  </Card>
</CardGroup>