About the service
I design and deploy production time-series platforms on TimescaleDB — a PostgreSQL extension that scales billions of rows without leaving familiar SQL. When Postgres tables bloat so inserts stall and dashboards timeout, I rebuild the storage layer around hypertables and restore predictable performance for sensor telemetry, server metrics, or exchange ticks.
Work covers the full data lifecycle. I organize data into hypertables with the right chunk interval, add continuous aggregates so minute, hour, and day rollups are precomputed and updated incrementally, and set up columnar compression that shrinks history by multiples while keeping it queryable. Retention policies automatically delete or archive old chunks, and index tuning, chunk sizing, and query plans keep reads and writes healthy as data grows. You get a documented schema, migration scripts, before-and-after benchmarks, and clear instructions. Typical tasks:
— hypertable design and migration from plain PostgreSQL;
— continuous aggregates and real-time rollups;
— compression, retention, and tiered storage policies;
— query and insert tuning with reproducible benchmarks.
Work covers the full data lifecycle. I organize data into hypertables with the right chunk interval, add continuous aggregates so minute, hour, and day rollups are precomputed and updated incrementally, and set up columnar compression that shrinks history by multiples while keeping it queryable. Retention policies automatically delete or archive old chunks, and index tuning, chunk sizing, and query plans keep reads and writes healthy as data grows. You get a documented schema, migration scripts, before-and-after benchmarks, and clear instructions. Typical tasks:
— hypertable design and migration from plain PostgreSQL;
— continuous aggregates and real-time rollups;
— compression, retention, and tiered storage policies;
— query and insert tuning with reproducible benchmarks.
