Tiger Cloud (Fully-managed TimescaleDB) - Annual Commit

Key Tiger Cloud / TimescaleDB use cases include:

• IoT and Industrial Monitoring: Companies ingesting sensor data from thousands to millions of devices (20M+ measurements per day)

• Financial Services: Trading platforms, Crypto and digital wallets, exchanges requiring analytics, time-weighted averages and real-time aggregations

• Energy and Utilities: Organizations analyzing production data, consumption patterns, and equipment performance

Key Tiger Cloud features:

Built on 100% unforked Postgres, Tiger Cloud is designed to support:

• Automatic partitioning (Hypertables): Turn any Postgres table into a hypertable that automatically partitions data by time (and optionally space/dimension) for fast ingest even as datasets grow.

• Hybrid row/columnar storage (Hypercore): Handle both transactional and analytical workloads efficiently.

• Native compression: Reduce storage costs and speed up analytics by compressing historical data up to 95%, while keeping it fully queryable through the same SQL interface.

• Precompute common analytical queries and refresh them incrementally (Continuous aggregates): Real-time dashboards stay responsive without batch jobs or materialized view maintenance.

• Independent storage and compute scaling: Scale compute resources and storage capacity separately based on actual workload needs and pay only for what you use.

• Automatic data tiering to Azure Blob Storage: Historical data automatically moves to low-cost object storage while remaining queryable. Reduce storage costs without losing accessibility.

• Native Hybrid Search: Combine high-performance HNSW vector search (pgvectorscale) with BM25 keyword ranking (pg_textsearch), without the complexity of managing a separate vector database or Elasticsearch cluster.

• Enterprise readiness: Production-grade uptime SLAs, regional data isolation, compliance certifications, backups, High Availability, and 24/7 support reduces operational risk.

Highlights:

• 100% unforked Postgres: Full SQL compatibility for rapid adoption and integration.

• Real-time analytics on operational data: hybrid row/columnar storage, incremental materialized views.

• Storage cost optimization: Native data compression (up to 95%).