Tiger Data (formerly Timescale) offers a comprehensive database platform designed to handle the most demanding workloads, focusing on time-series, analytics, and events data at petabyte scale. Built on the industry-standard PostgreSQL, Tiger Data extends the functionality of traditional relational databases, making them suitable for modern, high-throughput applications in industrial IoT, energy, crypto, and artificial intelligence. The platform provides a unified infrastructure that eliminates the need for fragmented data stacks by allowing developers to use SQL for time-series analytics, relational data management, and advanced search functionality, including vector and hybrid keyword-semantic search.

The core of the Tiger Data platform is its advanced storage architecture, which utilizes automatic partitioning through hypertables, row-columnar hybrid storage, and powerful columnar compression that can reduce storage footprints by up to 95%. These capabilities are supported by a suite of 200+ native SQL hyperfunctions that simplify complex time-series analysis without requiring specialized query languages or external tools. For production-ready workloads, the platform offers fully managed, elastic cloud services as well as self-managed enterprise options for air-gapped, on-premises, and edge environments, ensuring that critical data systems remain highly available, secure, and compliant with enterprise standards.

Some of the key features are:

• Automatic Partitioning: Transforms standard Postgres tables into time-based or ID-based hypertables for efficient ingest and predictable query performance.
• Columnar Compression: Achieves up to 95% reduction in storage size while maintaining direct query capability on compressed data.
• Hybrid Search: Integrates both BM25 keyword search and vector similarity search directly into the database, supporting hybrid RRF (Reciprocal Rank Fusion) queries.
• Continuous Aggregates: Provides incrementally updated materialized views for instant dashboards, allowing for real-time reporting on massive datasets.
• Tiered Storage: Automatically moves colder data to low-cost object storage while keeping hot data on SSDs for rapid access.
• Fluid Storage: Offers a distributed block layer for Postgres that provides true elasticity and independent scaling of storage and compute.
• Postgres Native: Maintains 100% compatibility with the PostgreSQL ecosystem, allowing developers to use existing drivers, ORMs, and tools without proprietary lock-in.
• Enterprise Capabilities: Includes automated backups, point-in-time recovery, multi-node high availability, and comprehensive monitoring dashboards.

Tiger Data operates by sitting at the center of the modern data stack, serving as a primary operational database that can ingest, store, and process massive volumes of sensor and application telemetry. It synchronizes seamlessly with lakehouse architectures and data streaming systems, providing a consistent source of truth for both operational applications and downstream analytical tasks. By leveraging the familiar PostgreSQL interface, teams can reduce infrastructure overhead and operational complexity significantly.

Some common use cases include:

• IoT Telemetry: Ingesting and querying millions of data points per second from smart grids, EV charging networks, and industrial sensors.
• Crypto Markets: Handling high-frequency trading data, order books, and blockchain analytics with sub-second latency and high durability.
• AI and Machine Learning: Storing vector embeddings for LLM-powered applications and building hybrid RAG (Retrieval-Augmented Generation) search systems.
• Finance and Fintech: Managing financial tick data, time-weighted averages, and real-time ledger reporting at scale.
• Industrial Operations: Monitoring well performance, pipeline integrity, and production systems in oil and gas and manufacturing environments.