Modernizing Industry Data Solutions

Sean Ely
Sean Ely

The sensors in modern industrial facilities constantly generate huge volumes of timestamped data – a perfect use case for a time-series database. This data is mainly collected or aggregated by monitoring, inspection, and analysis devices in the fields of manufacturing, energy, chemical, and construction. The size of these data sets is extremely large, and they are written to often but read infrequently.

The main issues with traditional solutions are as follows:

  • Low write throughput: Single-node systems are not able to provide the throughput necessary to write hundreds of millions of time-series data points.
  • High storage costs: Traditional solutions cannot effectively compress time-series data, causing increased data storage requirements.
  • High maintenance: Single-node systems require specialized database administrators to split tables and databases.
  • Poor query performance: Traditional systems do not perform well when aggregating and analyzing massive data sets.

The requirements for industrial customers are as follows:

  • Feature stability
  • High-performance data writes
  • High-performance queries, including querying new and historical data
  • Option to deploy in the cloud
  • Option to deploy on-premises
  • Horizontal scalability
  • High availability
  • Connection with big data platforms in a convenient manner

TDengine can provide the following benefits:

  • High performance: millions of concurrent writes, tens of thousands of concurrent reads, and the ability to handle a large number of aggregate queries without performance deterioration
  • High availability: cluster deployment, horizontal scaling, and no single points of failure provide a stable foundation for production environments
  • Reduced cost: low requirements on hardware resources and high compression ratio reduce hardware costs by 70%
  • High level of integration: message queue, stream processing, and caching features included with the database as a single solution
  • Easy to learn: familiar interface, SQL support, and no-code complex queries make it easier than other time-series database (TSDB) solutions for relevant personnel to pick up


  • Sean Ely
    Sean Ely

    Sean Ely is Head of Product at TDengine, focused on making TDengine the best time-series database for Industrial IoT. He has spent over a decade working with time-series data, starting as an integrator working on industrial controls and later establishing himself as a subject matter expert in emerging technology, innovation, and data science for the energy industry.