# Stream Postgres change data capture into Iceberg for Databricks MERGE

**You can tail a Postgres database's changes into an append-only
Apache Iceberg changelog — no Kafka, no Debezium cluster, no JVM —
and materialize current state in Databricks with one MERGE.**
`lakewright listen` connects to Postgres logical replication
directly, snapshots the table first (parallel chunked reads), then
streams inserts/updates/deletes as changelog rows.

## How is this different from Debezium?
Debezium requires Kafka + Kafka Connect — an operational platform of
its own. Lakewright is one process speaking Postgres's built-in
logical decoding (pgoutput/test_decoding; PG10+, nothing to install
server-side). Positions checkpoint two-phase: the LSN is confirmed
only after the Iceberg commit lands.

## What lands in the changelog?
One row per change: `_op` (I/U/D/R), `_position` (LSN), capture
time, key columns, and the row image. Deletes are appended `_op=D`
rows — the changelog is immutable and point-in-time reconstructable,
which auditors prefer to a mutated mirror.

## How do I get current state?
Downstream, where MERGE engines excel:
```sql
MERGE INTO orders t USING (
  SELECT * FROM orders_changes QUALIFY
    ROW_NUMBER() OVER (PARTITION BY id ORDER BY _position DESC) = 1
) s ON t.id = s.id
WHEN MATCHED AND s._op = 'D' THEN DELETE
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED AND s._op != 'D' THEN INSERT *;
```
Snowflake tasks/streams and dbt incremental models follow the same
shape. MySQL binlog sources work identically (`--source mysql`).

## FAQ
**Huge tables?** The initial snapshot reads in parallel key-range /
page-range chunks — sized for tables that don't fit one SELECT.
**Replication slot risk?** Slot lag/retained-WAL are first-class
metrics; the listener warns before a forgotten slot fills a disk.