Open‑Source, High‑Performance Simplified Solution for Time Series Data
TDengine™ is an open-source, cloud-native time series database (TSDB) optimized for Internet of Things (IoT), Connected Cars, and Industrial IoT. It enables efficient, real-time ingestion, processing, and monitoring of petabytes of data per day, generated by billions of sensors and data collectors.
TDengine is the only time series database that solves the high-cardinality issue, supporting billions of data collection points while outperforming other time series databases in data ingestion, querying and compression.
Through built-in caching, stream processing, and data subscription features, TDengine provides a simplified solution for time series data processing that significantly reduces system design complexity and operational costs.
Through native distributed design, sharding and partitioning, separation of compute and storage, Raft, support for Kubernetes deployment, and full observability, TDengine can be deployed on public, private or hybrid clouds.
Trusted by Developers from Startups to Giants
Over 200k Installations and 20k GitHub Stars
DJI Automotive processes autonomous driving data in milliseconds
Li Auto reduces IoT data storage and compute nodes from 17 to only 5
Siemens SIMICAS® solution simplifies industrial data workflows
Internet of Things:
OPPO lowers storage resources for IoT product data by 75%
TCL builds an energy conservation and management platform for Industry 4.0
RCT Power builds a platform for its residential energy storage system
Why Developers Choose TDengine
The core components of TDengine, including clustering, are all available under open-source licenses. It has an active developer community with 18k stars on GitHub and over 100k running instances worldwide.
TDengine significantly reduces the effort to deploy and maintain a time series database. It provides a simple interface and seamless integrations for third-party tools, giving users and developers easy access to data.
Through supertables, storage and compute separation, data partitioning by time interval, pre-computation, and other means, TDengine makes it easy to explore, format, and get access to data in a highly efficient way.
Why Decision Makers Choose TDengine
Reduce Time Series Data Processing TCO by 50% or More
Compared with other time-series data solutions, TDengine reduces TCO in four ways:
- Reduces hardware costs with higher performance
- Reduces learning costs with standard SQL support and easy integration with third-party tools
- Reduces system complexity and operating costs with a simplified, fully integrated solution
- Reduces overhead and overprovisioning costs with flexible pricing options
Time Series Database Usage Scenarios
Smart hardware, connected equipment, smart homes, smart cities, smart water
Smart manufacturing, process industry, production digitization, digital twin
IDC, host, virtual machine, network device, container, micro service monitoring
TBox data, battery management system, autonomous driving, fleet management
Clean energy, power distribution, smart meters, energy management
Stock, securities and crypto market data