Timescale is a cloud platform built on PostgreSQL that optimizes time-series data management through a hybrid row-columnar engine, enabling faster queries and significant storage savings. It addresses the challenges of ingesting and analyzing large volumes of live data, achieving query speeds up to 1,000 times faster and reducing storage requirements by 95% compared to traditional databases.
Funding
$180.6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
TGFounders
Product
Problem
Traditional PostgreSQL databases face challenges when dealing with high-volume, rapidly arriving time-series data, leading to slow query performance and inefficient storage utilization. Analyzing and managing this data at scale requires specialized optimizations beyond standard database capabilities.
Solution
Timescale is a cloud-based platform built on PostgreSQL, engineered to efficiently manage and analyze time-series, events, and vector data. Its hybrid row-columnar storage engine accelerates query speeds and reduces storage footprint compared to conventional databases. Timescale offers features such as automatic data partitioning via hypertables, columnar compression, continuous aggregates, and tiered storage, enabling users to ingest and query vast amounts of live data with millisecond latency. The platform provides a fully managed PostgreSQL experience, handling database tuning, backups, failover, and upgrades, allowing developers to focus on building applications rather than database operations.
Target Audience
Timescale targets developers and organizations building applications that rely on time-series data, real-time analytics, and AI, including those in IoT, finance, crypto, energy, manufacturing, and SaaS.
Features
- Hypertables: Automatic partitioning of time-series data for efficient ingestion and querying.
- Hypercore: Hybrid row and columnar storage for optimized query performance.
- Continuous Aggregates: Materialized views that are automatically refreshed for real-time analytics.
- Tiered Storage: Automatic tiering of older data to Amazon S3 for cost-effective, infinite scalability.
- pgvectorscale: Low-latency search and retrieval on billions of vectors.
- Vectorizer: Generate vector embeddings directly within PostgreSQL.
- Point-In-Time Recovery: PITR and instant forks for data recovery and testing.
- Built-in hyperfunctions: Library of 100+ SQL functions for time-series analysis.
- Deep query observability: Built-in performance insights for tracking slow queries.
- Collaborative SQL Editor: AI-powered SQL editor for faster query development.
- Robust Integrations: Support for streaming (Kafka, S3), analytics (Grafana, Tableau), monitoring (Datadog, Prometheus), and AI/ML (OpenAI, Hugging Face) tools.