Built for Plant Efficiency
01
TDengine securely connects to traditional historians and other data sources for ETL, making digital transformation easier than ever.
02
Deploy TDengine Enterprise on-premises, in a private cloud, or use the fully managed TDengine Cloud.
03
TDengine is purpose-built for handling massive datasets; store, process, and monitor up to petabytes of data per day.
Solution Architecture
Deploying TDengine reduces system complexity and costs for energy industry data operations
How TDengine Benefits You
01
Rapid and simple implementation makes integration with existing systems an easy task.
02
High availability eliminates single points of failure with cluster deployment and horizontal scaling.
03
Built-in stream processing, caching, and data subscription components reduce system complexity and costs.
Case Study
The Nevados team was impressed by the speed at which TDengine processed their queries compared with other solutions. They have had a good experience using the TDengine Data Explorer to develop their queries before adding them to Grafana for dashboard prototyping and ad hoc analysis.
Background
Nevados produces a single-axis solar tracker adopted for non-flat terrain that is deployed at utility-scale projects across the United States. The previous time-series data stack at Nevados consisted of AWS IoT Core for device interconnection and Telegraf for data collection, with Amazon DynamoDB running on top as their database solution.
- DynamoDB, being a key-value store, was not able to perform acceptably as the size of their time-series data set increased.
- DynamoDB was hard to query compared with SQL databases and the team had difficulty accessing the data they needed when they needed it.
Solution
As TDengine supports data ingestion from Telegraf, Nevados was able to reconfigure their existing stack to use TDengine without changing the underlying components.
