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

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

In modern IoT and industrial applications, **edge computing** enables real-time data processing, while **cloud storage** ensures centralized visibility and analytics. But how do you efficiently synchronize time-series data from distributed edge devices to a cloud platform?

Join this webinar to see **TDengine in action** as we deploy and synchronize multiple **edge instances** with **TDengine Cloud**. You’ll learn how edge nodes can operate independently, process data locally, and seamlessly sync to the cloud for global insights.

**Key Takeaways:**

✅ Setting up **TDengine at the edge** for local processing

✅ Synchronizing edge data with **TDengine Cloud SaaS**

✅ Running **real-time queries** across distributed environments

Whether you're working with **IoT, industrial automation, or large-scale monitoring**, this session will show you how to **deploy a high-performance edge-cloud architecture** with TDengine.


Deploy Edge–Cloud Synchronization with a Time-Series Database

Real-time data visualization is critical for monitoring systems, detecting anomalies, and making informed decisions. But building effective dashboards that provide clear, actionable insights can be challenging—especially when dealing with time-series data at scale.

**Grafana** has become the go-to open-source tool for time-series visualization, but making sense of large volumes of data isn’t always straightforward. How do you efficiently query millions of data points without slowing down your dashboards? What’s the best way to structure your data sources for real-time updates? How can you configure visualizations to highlight trends, detect anomalies, and surface key metrics without overwhelming users?

In this webinar, we’ll break it all down. We’ll start with a high-level overview of Grafana and how it fits into the time-series data ecosystem. From there, we’ll walk through a real-world use case, outlining the architecture and key concepts before diving into a hands-on demo. You'll see exactly how to build a Grafana dashboard—connecting to a time-series database, utilizing queries, and designing visualizations that deliver meaningful insights.

To make sure you can apply what you’ve learned, we’re providing a **Docker Compose setup** so you can try it yourself. Whether you’re just getting started or looking to refine your skills, this session will give you everything you need to start building real-time dashboards with confidence.


Building Real-Time Dashboards with Grafana for a Time-Series Database

Join us for an in-depth webinar on stream processing in time-series databases, where we’ll explore how real-time data ingestion, transformation, and analysis can unlock new efficiencies and insights.

Learn about key stream processing techniques, the challenges of handling high-velocity time-series data, and how modern databases optimize for real-time analytics.

Whether you’re working with IoT, financial data, or industrial monitoring, this session will provide practical insights and best practices to enhance your data pipeline.


Stream Processing in Time-Series Databases

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

Replays

Announcing TDgpt: AI Agent for Time-Series Analytics

Deploy Edge–Cloud Synchronization with a Time-Series Database