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Explore TimescaleDB for scalable, real-time data aggregation and analysis. Perfect for IoT, analytics platforms, and fintech applications.

TimescaleDB is an open-source time-series SQL database optimized for fast ingest and complex queries. It's built as a PostgreSQL extension and offers automatic partitioning across time and space, making it ideal for handling large-scale time-series data.

PostgreSQL ++ for time series and events
Engineered to handle demanding workloads, like time series, vector, events, and analytics data. Built on PostgreSQL, with expert support at no extra charge.

Key Features

  • Scalability: Automatic partitioning of data across time and space ensures efficient data management, even as datasets grow.
  • Full SQL Support: Enjoy the full capabilities of SQL for querying and managing data without learning a new query language.
  • Real-Time Aggregation: Provides real-time insights into data with continuous aggregates and hyperfunctions.
  • Compression: Offers significant storage savings through advanced columnar compression techniques.
  • High Availability: Ensures data reliability with built-in replication and failover capabilities.
  • Data Retention Policies: Easily manage data lifecycle with automated policies for downsampling and retention.
  • Seamless Upgrades: Upgrade your database without downtime or performance hits.
  • Broad Ecosystem: Integrates with the vast ecosystem of PostgreSQL tools, extensions, and resources.
  • Managed Service Option: Timescale Cloud offers a fully-managed service for those preferring not to administer their own database instance.
  • Vibrant Community: Join an active community with forums, Slack channels, and extensive documentation to help you succeed with TimescaleDB.

TimescaleDB Screenshots

Suggested Developer Use Cases

  • Data Analytics Platforms: Integrate TimescaleDB to manage large volumes of analytics data for business intelligence tools, providing faster insights with less overhead.
  • IOT Systems: Use TimescaleDB to store and analyze sensor data from IoT devices, leveraging its time-series optimization for real-time monitoring and reporting.
  • Fintech Applications: Incorporate TimescaleDB into fintech solutions to handle high-frequency trading data, ensuring robust performance during peak demand.
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