High-Performance, Scalable Time‑Series Database with SQL Support
Enables efficient, real-time data ingestion, processing, and monitoring of TB and even PB scale data per day, generated by billions of sensors and data collectors. TDengine can be widely applied to IoT, Industrial Internet, Connected Vehicles, DevOps, Energy , Finance and many other use-cases.
High Performance
TDengine’s innovative design and purpose-built storage engine outperforms other time-series databases for data ingestion, querying and data compression while significantly reducing storage and computing costs.
Scalability
TDengine’s native distributed architecture powers out-of-the-box scalability and high availability. Nodes can be added through simple configuration to achieve greater data processing power. In addition, this feature is open source.
SQL Support
TDengine uses SQL as the query language, thereby reducing learning and migration costs, while adding SQL extensions to handle time-series data better, and supporting convenient and flexible schemaless data ingestion.
Why Do Developers Choose TDengine Time Series Database?
High performance, scalability, and SQL support
Open Source
TDengine’s core modules, including cluster feature, are all available under open source licenses. It has 17.8k stars on GitHub and an active developer community.
All in One
With built-in caching, stream processing and data subscription functions, TDengine can work as a big data platform in many scenarios to reduce complexity and cost.
Zero Management
Installation and cluster setup can be done in seconds. TDengine’s running status can be monitored via Grafana or other DevOps tools.
Zero Learning Cost
With SQL as the query language, support for ubiquitous tools like Python, Java, C/C++, Go, Rust, Node.js connectors, there is zero learning cost.
Seamless Integration
Without a single line of code, TDengine provide seamless integration with third-party tools such as Telegraf, Grafana, EMQ X, Prometheus, StatsD, collectd, etc.
Interactive Console
TDengine Time Series Database provides convenient console access to the database to run ad hoc queries, maintain the database, or manage the cluster without any programming.
Usage Scenarios
Internet of Things
Smart hardware, connected equipment, smart homes, smart cities, smart water
Industrial Internet
Smart manufacturing, process industry, production digitization, digital twin
DevOps
IDC, host, virtual machine, network device, container, micro service monitoring
Connected Vehicles
TBox data, battery management system, autonomous driving, fleet management
Energy
Clean energy, power distribution, smart meters, energy management
Finance
Stock, securities and crypto market data
Selected Blogs
Using DBeaver in your TDengine deploymentDBeaver is a popular open-source tool used for database management and as an SQL client. This article explains how to use DBeaver with your TDengine deployment.Siemens SIMICAS® simplifies industrial time-series data processing workflowsIndustry needs end-to-end, interoperating solutions that bridge the gap between equipment and the Internet. See how Siemens used time-series technology to build such a solution.Environmental monitoring and data governance at scaleGESRI built a highly scalable and performant environmental monitoring and data governance system for real-time detection and early warning of anomalies in pollution discharge based on stringent government requirements.Logistics platform storage resources lowered by 60%When Lion Bridge Group’s current platform could not scale, they moved to TDengine which provided scalability while reducing hardware resources and saving costs.Announcing the TDengine Developer ConferenceThe TDengine Team is proud to announce that the TDengine Developer Conference will be held on August 13 at the Kuntai Gerry Culture Center in Beijing.DataX officially supports TDengine for better data migrationThe TDengine Team has extended DataX to work with TDengine, offering a good solution for time-series data migration.
TDengine is an open source time-series database with high performance, scalability and SQL support. In addition, it has built-in caching, stream processing, data subscription and other functions to reduce the complexity and costs of development and operations.
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