Products
Who We Are
TDengine™ is a time-series database purpose-built for Industry 4.0 and IIoT.
Our mission is to democratize industrial data systems and make big data accessible, valuable, and affordable for everyone.
!
On Premises
TDengine Enterprise
Deploy TDengine on the edge, on a local server, or in a public, private, or hybrid cloud
In the Cloud
TDengine Cloud
Run your industrial data workflows in our fully managed cloud service
Solutions
What We Do
TDengine is designed to help traditional industries achieve digital transformation and improve operational efficiency.
TDengine centralizes, stores, analyzes, and shares massive amounts of industrial data so you can scale your operations for Industry 4.0.
!!
By Industry
By Data Source
By Application
Resources
Why Developers Choose Us
* High Performance: outperforms other time-series databases in ingestion, querying, and compression.
* Cloud Native: enables scalability, elasticity, resiliency, observability, and automation in a distributed architecture.
* Ease of Use: reduces effort to deploy your time-series database, with simple, standardized interfaces and seamless integration.
Docs
Read the official documentation to learn about our products
Blog
View the latest articles about industrial data processing
Downloads
Download installation packages, special reports, and more
Tutorials
Follow along with step-by-step guides through the data lifecycle
Webinars
Watch live and on-demand webinars to learn more about TDengine
TDengine OSS
Explore our open source time series database project
# Nevados Streamlines Solar Tracker Data Operations with TDengine
Chait Diwadkar
January 9, 2024
 /
, , 
## Company Overview
 is a California-based company that helps solar project developers and contractors to simplify the design, installation, and operation of their solar power plants. With the overarching goal of making solar a cost-competitive source of clean energy from all angles, their products increase location site options, eliminate major costs of construction, and maximize power generation.
Nevados produces a single-axis solar tracker adopted for non-flat terrain that is deployed at utility-scale projects across the United States. To support the fully autonomous operation of solar power plants, their software controls and optimizes trackers on an individual row-by-row basis in real time. Their trackers collect moment-by-moment changes in weather conditions and detailed current generation data to produce a reliable solution that enables optimal energy production.
## Case Background
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, a key-value NoSQL database, was easy to get started, but its limitations became evident as Nevados completed more projects and collected more data.
Considering the nature of the time-series data that their products generated, a key-value store like **DynamoDB was not able to perform acceptably as the size of their data set increased**. In addition, they found DynamoDB **hard to query compared with SQL databases** and had difficulty accessing the data they needed when they needed it. Nevados realized that, for a growing business like itself, the solution was to replace its current database with a  purpose-built for industrial scenarios.
## Product Selection
Having decided to deploy a time-series database, Nevados compared several of the leading products on the market to determine which would be the best fit for their use case. They were highly impressed by the query performance of TDengine and its support for standard SQL. Furthermore, the distributed architecture of TDengine 3.0 would be suitable for their plans for future expansion.
Nevados was hesitant at first to try , which was newly released when this project started. Compared with the time-series database InfluxDB, TDengine had a smaller community and less comprehensive documentation. Nevados wondered whether they would have the resources needed to solve any problems that may occur. In addition, with TDengine Cloud being a new product, not all features and capabilities were fully in place from the beginning.
After working with the TDengine Team, however, both of their concerns were alleviated. First, the **personal level of support offered by team members enabled quick resolution of the issues** that they encountered. As a valued TDengine Cloud customer, Nevados communicated directly with the product and development teams and brought up their questions and concerns, being assured that TDengine Team members would address them as soon as they could.
Second, Nevados found the **pace of innovation at TDengine to be extremely fast**. In addition to addressing bugs, the development team at TDengine has worked tirelessly to improve the reliability and performance of the product. Nevados has been delighted on many occasions to find that a hotfix can be provided in a short time frame and that **new features are constantly being added to the product**.
In particular, with the data sharing feature of TDengine Cloud, Nevados can apply fine-grained user and data sharing privileges, which has **greatly improved their overall experience and streamlined operations** related to database administration. The enhanced user management system was prioritized on Nevados’s request and the entire development process was completed in 2 months.
## Project Results
After deciding on TDengine as their time-series database, Nevados adjusted their stack such that data being collected by Telegraf was written to a TDengine Cloud instance.
As TDengine supports data ingestion from , Nevados was able to reconfigure their existing stack to use TDengine without changing the underlying components. 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  for dashboard prototyping and ad hoc analysis.
At present, Nevados has already migrated all the data from their previous solution to TDengine. Going forward, they plan to take advantage of the distributed architecture of TDengine and deploy TDengine Enterprise instances at project sites that replicate data from the edge to a centralized TDengine Cloud instance. Furthermore, TDengine will enable them to further simplify their data stack in the future by moving to . We at TDengine are excited to see the continuing business growth at Nevados and look forward to supporting their time-series data processing needs.
[Download TDengine OSS and get started in 60 seconds
Download Now
[Contact us to learn more about TDengine Enterprise
Contact Us
[Register for a free TDengine Cloud account today
Register for Cloud
* !!
  Chait Diwadkar
  Chait Diwadkar is Director of Solution Engineering at TDengine. Prior to joining TDengine he was in the biotechnology industry in technical marketing, professional services, and product management roles and supported customers in pharma, medical devices and diagnostics on analytical chemistry, and genetic platforms.
Subscribe to our newsletter
Search
TDengine™ is a time-series database purpose-built for Industry 4.0 and Industrial IoT. It enables real-time ingestion, storage, analysis, and distribution of petabytes of data per day, generated by billions of sensors and data collectors. With TDengine making big data accessible, valuable, and affordable, digital transformation has never been easier.
© 2022–2025 TDengine
Learn
Company
Social
Search for:
# Gotion Elevates Energy Storage with TDengine Cloud
Zhicheng Liu (Gotion)
June 30, 2024
, 
Highlights
* In Gotion’s production instance, TDengine ingests **millions of records per second** and compresses data to **10% of its original size.**
* Gotion’s team can **focus on their business requirements** with the fully managed TDengine Cloud taking care of database administration and management tasks.
* Private links to TDengine Cloud in AWS **significantly reduce network latency** and bandwidth costs.
In one of our energy storage projects, we needed a way to monitor the safety of our batteries in real time. The data we were collecting — battery charging and usage data, current, voltage, and so on — were all classic examples of time-series data. With massive amounts of time-series data expected to come in from our users, we decided to deploy a purpose-built time-series database.
## Background
We deployed TDengine 2.x on premises in 2022, but coordinating with our maintenance teams proved to be difficult. At the time, we were using the open-source TDengine OSS, which is not managed and does not include dedicated support, and our staff were spending much time learning and deploying the product. It was then that we learned of TDengine Cloud, and what excited us most about the platform was the fact that it is fully managed.
With TDengine Cloud, we received the same feature set as TDengine Enterprise but were freed from the burdens of database deployment, optimization, backup, and disaster recovery — allowing us to focus all our attention on our core business. As we were still in the early stages of the project, we found that the Starter package listed on TDengine Cloud was a good fit for our requirements.
We created a supertable in TDengine for each type of battery in the project, and then created a table for each battery within those supertables. The supertables each have over 100 columns, including metrics such as electricity usage and charging status. Our batteries generate a data point every 10 seconds 24 hours a day, which is written to TDengine. We can easily view the metadata for our tables and supertables in the Data Explorer within TDengine Cloud.
## Data Ingestion Performance
TDengine is designed based on the characteristics of time-series data and innovates in several areas — columnar compression, flexible tags, and one table per device, for example. By using TDengine, we could avoid issues such as write locks, low compression ratios, and duplicated static data.
The data generated by our storage devices is transmitted over MQTT into the system, where business data is forwarded to PostgreSQL and time-series data — device-generated data as well as logs and statuses — is sent to TDengine. Each system in the middle tier uses the data stored in these databases for analysis and computation but may also send commands directly to devices. Finally, the data is displayed on management platforms.
During our testing, TDengine was able to ingest millions of data points per second and compress our datasets to 10% of their original size. These results were reflected in production as well.
## Query Performance
We perform many kinds of queries on our data in order to provide the best user experience and after-sales service. These can include monitoring battery health, analyzing trends in battery usage, and estimating product lifetime, among others.
Several typical queries are described as follows:
1. We use session windows to calculate the sum of the power output of a single device in intervals less than or equal to 1 minute: `SELECT FIRST(ts) firstTs, LAST(ts) lastTs, COUNT(*) countVal, SUM(input_total_power) totalPower FROM device_data_58CF7920B63C WHERE ts >= '2023-08-09T00:00:00.000Z' AND ts< '2023-08-10T00:00:00.000Z' SESSION(ts, 1m);`
2. We use time windows (`INTERVAL`) to query trends in device power output with the `OFFSET` parameter to take time zones into account: `SELECT _WSTART AS ts, AVG(inverter_power) avgVal, MIN(inverter_power) minVal, MAX(inverter_power) maxVal FROM gendome.device_data_58CF7920B63C WHERE inverter_power > 0 AND ts >= '2023-08-09T00:00:00.000Z' AND ts< '2023-08-10T00:00:00.000Z' INTERVAL (1d, 960m) FILL (NULL);`
Because our applications are in the same AWS region as our TDengine Cloud deployment, we can create private links between them and TDengine so that our data is not transmitted over the Internet. This greatly decreases ingestion latency and also reduces costs associated with network traffic.
## Conclusion
TDengine Cloud helps us run our project at minimal cost thanks to its reasonably priced, fully managed enterprise service. It has greatly improved our efficiency and given us the flexibility to scale out at any time that our requirements might demand it. Going forward, we plan to explore the capabilities of TDengine’s data sharing and stream processing features as well.
## About Gotion
Gotion is a leading global solutions provider in the renewable energy sector. The company focuses on researching, producing, and marketing lithium-ion batteries and electricity storage.
  Zhicheng Liu (Gotion)
  Zhicheng Liu is a platform architect at Gotion and has many years of experience in the energy IoT field.