TDengine Cloud Data Management
Simplified time-series data solution with simplified management
Simplified solution
To simplify system design and reduce operation costs, TDengine integrates its time-series database with built-in caching, stream processing, and data subscription components that take full advantage of the characteristics of time-series data. This integrated design goes beyond the efficient storage and analysis of time-series data to provide a comprehensive and simplified solution for time-series data processing.
- With built-in caching, you can find the current status of your data collection points without deploying specialized tools like Redis.
- With built-in stream processing, you can define stream transformations in SQL without adding Spark or Flink to your pipeline. Incoming data is automatically processed in real time, and the results are pushed to specified tables based on triggering rules that you define.
- With built-in data subscription, your applications can obtain data on a pub/sub model from a database, a supertable, a set of tables, or a single table. TDengine provides a familiar Kafka-like API, but you can use SQL to set filtering conditions and perform aggregation, roll-up, or transformation on your subscription.
Simplified management
TDengine Cloud saves you time and resources by providing a fully managed time-series data platform with scalability, elasticity, resiliency, and observability.
- TDengine has a native distributed design that supports up to 10 billion data collection points. As your business grows, more computing and storage resources can be added automatically without disrupting system operations.
- TDengine separates storage from computing by introducing the compute node (qnode). This makes it an ideal analytics platform for time-series data, including real time analytics and batch processing, because the compute resources in the cloud are elastic and essentially infinite.
- TDengine provides storage reliability through write-ahead logs and high availability through data replication and the RAFT consensus algorithm.
- TDengine provides observability through built-in monitoring of the operating status and metrics for your cluster. Any alerts generated are handled automatically by TDengine Cloud so that you can focus on your core business.
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