Using PostgreSQL in Your Language: TimescaleDB Integration Guides
🧑💻 Developers choose PostgreSQL for a reason.
It’s reliable. It’s expressive. It has real indexes, real transactions, and real SQL.
It’s the kind of tool you can bet your infrastructure on.
But when your application starts pushing more data—tracking events, ingesting logs, storing time-series measurements—things get more complicated.
Query performance drops. Dashboards lag. Ingest pipelines need duct tape.
And suddenly, you're patching around a system that wasn’t built for high-ingest or real-time workloads.
Timescale extends Postgres for those use cases.
It’s not a new database. It’s not a wrapper.
It’s PostgreSQL, with added capabilities designed for:
- Time-series data
- Real-time analytics
- Large-scale inserts and queries
- Compression and storage efficiency
- Vector and AI workloads (if you're heading there)
And because it’s a Postgres extension, your tools still work. Your queries still work. Your brain still works.
Why this series exists
PostgreSQL is language-agnostic.
But that doesn’t mean every language community has what they need out of the box.
We put this series together to show how to connect your language of choice to Postgres (with Timescale enabled) in a way that’s:
- Developer-friendly
- Practical for production
- Tuned for real workloads
No new stack to learn. Timescale makes the one you have better because it's still Postgres under the hood. You keep the parts that already work—and get more where it counts.
