TDengine Listed In The Information’s Top 50 Most Promising Startups 2022 Tdengine Database

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Most importantly, TDengine 3.0 helps you gain insight into your time-series data with analytical capabilities comparable to relational databases and support for standard SQL syntax with time-series-specific extensions.

Unfortunately, most databases are just "cloud-ready", not cloud-native. When you buy the cloud service provided by some database vendors, such as TimescaleDB, you need to tell the system how many virtual servers (including the CPU and memory configuration) and how many gigabytes of storage you want. Even if you don’t run any queries, you’re still forced to pay for the computing resources, and if your data grows in scale, you need to decide whether to buy more resources. By offering this kind of cloud solution, database service providers are really just reselling cloud platforms.

The Top 50 Most Promising Startups is a yearly collection of up-and-coming enterprises in fields such as data, fintech, and cybersecurity that are considered by industry sources to have the most potential for future growth and valuation.

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Today TDengine, tdengine database the open-source, cloud-native time-series database optimized for IoT, was named one of the Top 50 Most Promising Startups by tech sector-oriented media organization The Information.

When I first started TDengine back in 2017, my main concern was whether the market still had room for another time series database. I constantly pondered whether existing databases were good enough to run time series applications, and whether they already met business needs. Even today, this is still something that I think about all the time, asking myself: is this time series database really worth my dedication and obsession?

InfluxDB, Prometheus, TimescaleDB, and TDengine all support continuous query. This is very useful for monitoring dashboards, as all of the charts and diagrams can be updated periodically. But not all data processing requirements – ETL, for example – can be met by continuous query alone, and time-series databases need to support event-driven stream processing.

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Separation of compute and storage: In containerized environments, specific containers may be up or down at any time, but stored data is persistent. Traditional database designs are unable to cope with this because they store data locally. Furthermore, to run a complicated query or do batch processing, more compute nodes need to be added dynamically to speed up the process.
Elasticity: The system must be able to adjust storage and compute resources based on workload and latency requirements. For compute resources, this is not a hard decision for the system to make. But storage resources are another story. To scale a distributed database up or down, its shards have to be merged or split while live data is coming or queries are running. Designing a system that can accomplish this is no easy task.
Observability: The status of a time-series database must be monitored together with the other components of the system, so a good database needs to provide full observability to make operations and management simpler.
At this point, any newly developed time-series database must be cloud native. While TDengine was designed from day one with a highly scalable distributed architecture, as of version 3.0 we now fully support the separation of compute and storage.