The engines complement each other more often than they compete. Choose by workload:
| Task | Choose | Why |
|---|---|---|
| Application backend, many users | PostgreSQL | Concurrent writers, row locks, constraints |
| Point lookups, many small writes | PostgreSQL | B-tree indexes, sub-millisecond reads |
| Ad hoc analysis on a laptop | DuckDB | No server; 10-30x faster scans here |
| Querying Parquet 129 , CSV, JSON in place | DuckDB | Readers and pushdown built in |
| Pipeline transformation steps | DuckDB | In-process, fast, files in and out |
| Shared dashboards on fresh OLTP data | PostgreSQL | Matviews (Views, Partitions, Parallelism), one source |
| Data beyond one machine | Neither | Warehouse or lakehouse (Cloud Data Warehouses, Lakehouses, Data Quality and Governance) |
For BookNest, orders stay in PostgreSQL, where the shop writes them; the sales mart built from them is analyzed in DuckDB files or Parquet, with PostgreSQL materialized views for minutes-fresh dashboards. Transforming Data with dbt builds the mart with dbt 37,942 on both engines; ClickHouse adds ClickHouse 29,491 .