Production-grade patterns for engineers who run time-series data at scale — from raw telemetry ingestion to optimized, compliant, automated storage.
The TimescaleDB Lifecycle & Automation Hub exists to help time-series data engineers, IoT platform developers, Python automation builders, and DevOps teams design, automate, and monitor production-grade time-series data lifecycles. It bridges the gap between raw telemetry ingestion and optimized, compliant, automated storage using TimescaleDB continuous aggregates, retention policies, and Python orchestration.
Every guide is built around deterministic, idempotent patterns you can put straight into production: hypertable partitioning and chunk sizing, continuous aggregate creation and refresh scheduling, retention and compression automation, and the Python ETL and monitoring glue that keeps it all observable. The focus is on operational correctness — sequencing compression before retention, aligning refresh offsets with chunk boundaries, and scoping background workers to least privilege.
Browse the three pillars below, or jump into the full topic index to explore every guide on partitioning, aggregates, and lifecycle automation.
Create, refresh, and schedule materialized rollups reliably.
Partitioning, chunk sizing, compression models, and access boundaries.
Automate TTL enforcement, compression, and the full data lifecycle.