TDengine 3.0 Launch Incorporates Cloud-Native Architecture, Merging Scale and Simplicity for Time-Series Data Management

TDengine, an open-source data platform purpose-built for time-series data in IoT applications, is debuting TDengine 3.0, the latest update for the enterprise’s time-series data management solution. As a database with caching, stream processing, and data subscription compiled into its open-source solution, TDengine 3.0 is now formatted with a cloud-native architecture for Kubernetes deployments, optimizing scale and simplicity of deployment and management. The release tackles major pain points for users with increasing amounts of data resulting from large-scale IoT deployments, according to the company.

“TDengine 3.0 has superior scalability and can support billions of IoT sensors and data collection points while outperforming other time-series databases regarding data ingestion rate and query response,” said Jeff Tao, CEO and founder of TDengine. “It is the first time-series database in the world to solve the high cardinality issue for time-series data processing and the only time-series database on the market which supports the required components for big data platforms.”

The update comes stocked with major features and functions, immediately available to its users. The release now offers Kubernetes and Serverless container support, separating compute and storage resources for dynamic scaling. TDengine 3.0’s scaling capabilities are highly emphasized as a major feature of the release, where high-cardinality issues are eliminated by TDengine’s clusters; a single cluster can contain billions of time-series data points without sacrificing start-up times. High performance on time-series data casts TDengine as an efficient platform, with 2-5x the speed of other time-series databases and 10x the read and write performance compared to general databases, according to the company.

Cloud-native architecture was a highly requested item for TDengine’s latest release; this addition makes deployment possible on public, private, or hybrid clouds, offering a variety of use cases. Users without the support of a team on big data platforms will find TDengine 3.0’s simplicity, in both its use and maintenance, optimal for their enterprise structure. Users with substantial amounts of devices or data collection points are great candidates for TDengine 3.0, as its cluster efficiency can handle that sort of large data.

“With more than 19,000 stars on GitHub, TDengine has nearly 140,000 instances in more than 50 countries worldwide. TDengine has over 370,000 lines of code and over 230,000 lines of testing code. Its unique storage engine design makes it ideal for time-series data processing,” said Tao. “Additionally, since it is a cloud-native database, both compute, and storage resources can be dynamically changed based on the workload and also provides the ability to pay-as-you-go to cut the operation cost, which benefits all users.”

In the future, TDengine will look to increase support for third-party tools such as BI, visualization, and data collection agents, as well as offer support for abnormal detection and time-series forecasting, according to the company.

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