Executive Summary
Nevados Engineering is a California-based producer of single-axis solar trackers. As the company expanded, the time-series data generated by these intelligent trackers quickly outgrew the capabilities of their original architecture — AWS IoT Core → AWS Lambda → DynamoDB — which was simple to start with but difficult to query, hard to scale, and unable to support the analytical depth required for a rapidly growing renewable energy business.
Migrating to TDengine solved these challenges. With minimal changes to their pipeline, Nevados gained fast SQL analytics, stream processing, high compression, and a fully managed infrastructure. As Nevados rapidly scaled from 4,000 to over 34,000 trackers, representing more than 1 GW of capacity, hundreds of millions of rows per day are ingested into their TDengine Cloud deployment with sub-100 ms query latency
With TDengine, Nevados enjoys real-time visibility across all sites, automated anomaly detection, predictive maintenance capabilities, easy data sharing with partners, and dramatically reduced operational overhead. TDengine has become the data backbone enabling Nevados to scale confidently and operate their nationwide solar tracker fleet with precision and efficiency.
From Complex Terrain to Data-Driven Precision
Solar developers have typically avoided hilly, uneven land because traditional single-axis trackers can’t handle slopes without expensive grading. Nevados Engineering, based in Berkeley, California, changed that.
Their TRACE™ All-Terrain Tracker automatically adapts to each site’s unique topography, allowing solar plants to be built on rolling hills, irregular landscapes, and previously “unbuildable” terrain. Each row of panels moves independently, guided by a sophisticated control system that monitors and adjusts every tracker’s tilt, torque, and orientation to maximize yield while protecting mechanical components from stress.
This fine-grained intelligence comes at a cost: data — and lots of it.
Every tracker in the field generates about 50 telemetry metrics every few minutes, ranging from actuator torque and motor temperature to irradiance, voltage, and fault diagnostics. Multiply that by thousands of trackers spread across multiple projects and time zones, and Nevados was quickly drowning in data.
Too Much Insight, Too Little Structure
Back in 2023, Nevados managed roughly 4,000 trackers across their early deployments. Their architecture at the time was:
AWS IoT Core → Lambda → DynamoDB
DynamoDB provided scale but lacked efficient time-window analytics and cross-site correlation. It was difficult or impossible to run analyses like:
- “Which rows lag behind after cloud cover clears?“
- “How does tracker torque vary by slope angle?“
- “Which zones stow too often under similar wind thresholds?“
DynamoDB, a key–value NoSQL database, offered scalability but not query flexibility. There was no efficient way to run time-windowed, cross-site analysis or real-time aggregation.
As Nevados’s growth accelerated, DynamoDB’s performance was unable to keep up. The company’s maintenance workload skyrocketed, and infrastructure complexity became a daily burden. The team knew they needed something built for time-series data at industrial scale: a platform that could combine speed, simplicity, and scalability without requiring a DevOps army to run it.
The Turning Point: Moving to TDengine Cloud
In 2023, Nevados discovered TDengine Cloud, the fully managed, SaaS version of the TDengine time-series database. It promised what the team needed most:
- Native SQL support for fast, intuitive analytics
- Stream processing directly in the database
- High compression to store years of telemetry data affordably
- Zero maintenance, thanks to its cloud-native architecture
Within weeks, Nevados pointed its existing Telegraf collectors to TDengine Cloud. The transition was nearly seamless: no code rewrites, just new connection strings and schema definitions. By the end of the first month, all 4,000 trackers were streaming data into TDengine Cloud, and the engineering team could query billions of records instantly through TDengine’s Data Explorer or Grafana dashboards.
Each tracker contributes around 50 metrics every 5 minutes, including mechanical, electrical, and environmental data. That’s over 17 billion records per month, all automatically compressed, indexed, and available for instant analysis in TDengine.
Scaling Up: From 4,000 to 34,000 Trackers
Over the following two years, Nevados’ fleet exploded from 4,000 to more than 34,000 trackers, representing over 1 GW of DC capacity across dozens of solar sites in the United States. Despite this 8× growth, TDengine has scaled effortlessly, handling hundreds of millions of new rows per day while maintaining query latency under 100 milliseconds (P99).
Growth Snapshot
| Metric | 2023 | 2025 |
|---|---|---|
| Trackers | 4,000 | 34,000+ |
| Fleet capacity | ~100 MW DC | > 1 GW DC |
| Metrics per tracker | 50 | 50 |
| Data per day | ~50 million rows | Hundreds of millions |
| Query latency (P99) | — | < 100 ms |
| Platform | DynamoDB (self-managed) | TDengine Cloud (fully managed SaaS) |
Patrick Heneise
Director of Software Engineering
Nevados
TDengine Cloud has been instrumental in our ability to scale Nevados’s operations. In less than two years, we grew from managing 4,000 trackers to over 34,000 — each streaming dozens of metrics — and TDengine handled it seamlessly. Now we have real-time visibility across more than a gigawatt of solar assets, and our engineers can run analytics on billions of data points whenever they need to.
Operational Transformation
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Real-time visibility: Every solar site connected to TDengine now streams live data into the platform. Engineers and customers can open Grafana or the Nevados dashboard and instantly view:
- Tracker uptime and performance
- Fault and alarm distribution
- Backtracking and stow compliance
Previously, these insights took hours of manual data exports; now they’re instantaneous.
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Predictive maintenance and anomaly detection: TDengine’s stream processing framework computes KPIs on the fly, detecting anomalies such as:
- Rows that deviate from expected tilt profiles
- Torque motors drawing excessive current
- Trackers stowing unnecessarily due to false wind events
- Site-level underperformance due to dirty panels
This early warning system helps maintenance teams fix issues before production loss, rather than after the fact.
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Data sharing and collaboration: Using TDengine’s built-in data sharing feature, Nevados grants controlled access to project developers, EPCs, and asset owners. Instead of building a custom API gateway or exporting CSVs, each stakeholder gets secure, direct access to their own datasets within TDengine Cloud. That means no data duplication, no stale reports — and a better experience for every customer.
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Simplified operations: Because TDengine Cloud is fully managed, Nevados doesn’t need to worry about infrastructure scaling, database tuning, or backup routines. Their small engineering team stays focused on improving the tracker and software experience, not managing databases.
Reliability & High Availability for a 1 GW Fleet
As Nevados’s tracker fleet surpassed 1 GW, reliability and continuity became just as important as query speed. To support this scale, TDengine Cloud provides a fully managed deployment with replicated storage, automated failover, and continuous backups handled by the TDengine operations team. This ensures database reliability.
While Nevados does not operate the underlying clusters themselves, their TDengine Cloud environment runs across multiple nodes with built-in redundancy, giving the engineering team confidence that data remains available even during infrastructure failures. Routine backup validation, system monitoring, and incident response are managed by TDengine as part of the service, allowing Nevados to focus on product development rather than database operations.
Business Outcomes
| Category | Before TDengine Cloud | With TDengine Cloud |
|---|---|---|
| Scalability | Struggled past 4,000 devices | Effortlessly handles 34,000+ |
| Data management | Manual rollups, limited retention | Full telemetry retained in compressed form |
| Query performance | Minutes or timeouts | Milliseconds |
| Maintenance | Heavy DevOps overhead | Zero — fully managed SaaS |
| Customer access | Manual exports | Instant, secure sharing |
| Insight cycle | Post-hoc reports | Real-time decisions |
The Human Side of Data
Behind the technology is a simple goal: to make solar work everywhere. Nevados’s trackers make it possible to install clean energy projects in places where geography once said “no.”
TDengine Cloud ensures those projects are monitored, optimized, and continually improved through data, without adding operational complexity.
Sam Prest
VP Product & Engineering, Software
Nevados
We started with a few thousand trackers and a lot of growing pains. TDengine gave us instant scalability and reliability. Now we’re past 34,000 trackers and over a gigawatt — and everything just works.
Why This Matters to Renewable Operators
Nevados’s story is a blueprint for every company operating large, distributed renewable fleets:
- Don’t fight your database. Use one designed for time-series data.
- Let SaaS handle scale. TDengine Cloud grows automatically with your assets.
- Keep data open. SQL + built-in sharing enable collaboration across engineering, analytics, and operations.
- Focus on performance, not infrastructure. The best time-series database is the one you never have to babysit.
Patrick Heneise
Director of Software Engineering
Nevados
The performance and reliability of TDengine mean we spend less time on database issues and more on optimizing our product. It’s truly become a backbone of our digital operations.
About Nevados
Nevados Engineering designs and manufactures the TRACE™ All-Terrain Tracker, the only solar tracker optimized for steep and uneven terrain. With patented terrain-aware algorithms and row-level control, Nevados enables cost-effective solar deployment on land once considered unusable. The company’s systems now power over 1 GW of solar capacity across the United States.
For more information, visit https://nevados.solar.
About TDengine
TDengine is an AI-powered data platform designed for industrial applications, combining the high-performance time-series database TDengine TSDB and the AI-native data management platform TDengine IDMP. With TDengine TSDB handling data ingestion, storage, and processing, and TDengine IDMP providing contextualization, standardization, and AI-powered analytics, TDengine enables industrial enterprises to unlock the true value of their time-series data.
