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.

TDengine

Curious about forecasting time-series data without diving deep into complex machine learning pipelines? Join us for a practical, hands-on webinar exploring **TDgpt**, the built-in AI engine for time-series analysis in TDengine. We’ll walk through real-world forecasting examples using just SQL - no Python scripts or external ML frameworks required.

In this session, you’ll learn how to:

*   Understand the basics of TDgpt and its built-in forecasting capabilities
*   Prepare and query time-series data using TDengine’s SQL extensions
*   Generate predictive insights and visualizations using TDgpt forecasting
*   Interpret results to make data-driven decisions for metrics like CPU usage, energy consumption, or IoT device behavior

Whether you're a developer, data analyst, or infrastructure engineer, you’ll leave with practical knowledge and a working setup you can replicate in your own environment. Expect live demos, example datasets, and a downloadable Docker Compose environment to get started fast.


Hands-on Time-Series Data Forecasting with TDgpt

Anomaly detection is key to identifying performance issues, failures, and unusual patterns in your infrastructure before they become critical. In this webinar, we’ll take a practical look at how to detect anomalies in time-series data using TDengine—built around real-world metrics from EC2 instances.

Through a hands-on, live demo, you’ll learn how to:

*   Ingest and structure time-series data from cloud environments
*   Use SQL to identify spikes, drops, and outliers in key metrics
*   Visualize system health and anomalies in real time

This session will equip you with practical techniques to monitor and react to anomalies using TDengine's high-performance time-series engine. Join us to see real-time anomaly detection in action: no theory, just practical, repeatable steps.


Hands-on Anomaly Detection with TDgpt

The TDengine team is delighted to announce the release of TDgpt, an AI agent that makes time-series analytics accessible to all. With TDgpt, just a single SQL statement puts the power of time-series foundation models, LLMs, machine learning models, and statistical algorithms at your fingertips, and time-series forecasting and anomaly detection are easier than ever before.

Highlights include:

*   **Zero learning curve:** Use advanced algorithms and models without needing to understand implementation details - just simple and familiar SQL syntax.
*   **Zero initial investment:** TDgpt is free, open-source software and includes multiple analytics models and algorithms out of the box so that you can find what works best for your business.
*   **Zero wasted time:** Instead of wasting model development resources on data engineering, simply update models in TDgpt and see results instantly reflected in your applications.

Join us on Thursday, March 27 at 3 p.m. PDT as Jeff Tao, founder and CEO of TDengine, unveils this innovative product and describes how you can use it to revolutionize data engineering in your business.


Announcing TDgpt: AI Agent for Time-Series Analytics

Managing high-frequency telemetry from EMS (Energy Management Systems) and BESS (Battery Energy Storage Systems) shouldn’t be a bottleneck. Join us for a deep dive into how TDengine helps leading energy storage operators overcome the toughest challenges in data ingestion, long-term storage, and real-time analytics — all while reducing costs.

In this session, we’ll walk you through:

*   The top 5 data challenges in BESS operation & maintenance
*   Key metrics to evaluate time-series database performance
*   Real-world examples from battery storage system deployments
*   A **live demo** showing 400,000 data points per second — with real-time queries, dashboards, and edge-cloud sync

Whether you're evaluating your next database or scaling an existing deployment, this session will offer practical insights and proven performance benchmarks to take back to your team.

**Who Should Attend:**

Data architects, software engineers, BESS operators, and decision-makers exploring scalable, cost-effective time-series data solutions for energy systems.


Can Your Database Handle Battery Telemetry at Scale?

Replays

Hands-on Time-Series Data Forecasting with TDgpt

Hands-on Anomaly Detection with TDgpt