TDengine

The AI-powered data historian

[TDengine @TDengineDB]

We’re excited to announce our partnership with Chile-based Piramid, expanding TDengine’s presence in Latin America.

With strong expertise in industrial data and analytics, Piramid will help bring scalable, affordable, and AI-native industrial data solutions to mining and other enterprises across the region, supporting modernization efforts and enabling real-time, data-driven operations.

This collaboration is another step in growing the TDengine ecosystem and delivering modern data infrastructure to new markets.

Read the full announcement: https://t.co/gAYpN47onv

[TDengine @TDengineDB]

What does the future of industrial data look like in the age of AI?

In this 12-part blog series, TDengine founder Jeff Tao lays out a clear vision for an AI-native industrial data foundation, rethinking how data is structured, connected, and used to drive intelligent operations. The series challenges conventional architectures and offers a new perspective on how industrial data can become a true strategic asset.

If you’re thinking beyond incremental improvements and toward what’s next, this is a series worth your time.

Read the full series: https://t.co/nSe1H0DwPR

[TDengine @TDengineDB]

We’re excited to share highlights from the latest TDengine IDMP release, bringing smarter analytics and faster insights to industrial data.

This update introduces enhanced capabilities across AI insights, visualization, event analysis, process analytics, data standardization, and localization - helping teams turn complex data into actionable intelligence more efficiently.

Read more: https://t.co/ZIS0Wo9uZK

[TDengine @TDengineDB]

Modernizing industrial data starts with the right foundation, and time-series databases (TSDBs) play a critical role.

In this article, Jeff Tao explores the shift from legacy data archives to TSDBs, and why they’re essential for handling scale, performance, and real-time access to industrial data.

At the same time, TSDBs are just one piece of a larger transformation; building a true industrial data foundation means combining high-performance data infrastructure with context, analytics, and AI.

The opportunity now is to use TSDBs as a solid core and then build on top to unlock AI-driven intelligent operations.

Read the full article: https://t.co/w4V3iJLpFT

[TDengine @TDengineDB]

Don’t miss this chance to see what’s possible with your industrial data.

Join our webinar on TDengine IDMP visualizations next Wednesday and learn how to turn complex operational data into clear, real-time insights. We’ll show you how to build dashboards and panels that actually support operations.

Register today:

https://t.co/KBZQfU2Z1e

[TDengine @TDengineDB]

Industrial data is evolving, and so is the role of the data historian.

In his latest article, TDengine founder & CEO Jeff Tao explores the shift from traditional historian architectures to an AI-native industrial data foundation, built to handle today’s scale, complexity, and real-time demands.

Instead of siloed systems and passive dashboards, modern platforms like TDengine unify data ingestion, storage, analytics, and AI, enabling real-time insights and smarter decision-making across operations.

Data today needs to be accessible, contextualized, and actionable - not just stored in a database.

Read the full article: https://t.co/6VvaOi4uT2

[TDengine @TDengineDB]

Monitoring complex chemical manufacturing processes doesn’t have to be overwhelming.

In this demo, we show how TDengine IDMP visualizations, including P&ID-style panels, enable real-time monitoring of critical systems, giving teams clear visibility into operations and faster insights when it matters most.

https://t.co/UDdAUBRgeR

[TDengine @TDengineDB]

In this TDengine IDMP demo, we walk through OPC UA data ingestion in TDengine IDMP.

See how industrial data from OPC UA servers can be connected and streamed directly into the historian for immediate use in panels, analytics tasks, and more.

https://t.co/j9s7vSUQbW

[TDengine @TDengineDB]

In this TDengine IDMP demo, we explore libraries, including templates, categories, units of measurement, and more. These provide the shared structures that help keep industrial data models organized and reusable and make it easier to build consistent asset models and analytics across projects and sites.

https://t.co/D5dcCkY9Pz

[TDengine @TDengineDB]

In his latest article, Jeff Tao explores why asset-centric data modeling is becoming even more important in the AI era for industrial operations.

Industrial data is inherently tied to assets, processes, and operational context. Without that structure, even advanced analytics and AI struggle to interpret what the data actually means. Modern platforms that focus only on queries and dashboards often miss this deeper layer of context.

As AI becomes more integrated into industrial systems, modeling data around assets and operations may be the key to making those systems truly intelligent.

Read more: https://t.co/uXaOkpaH5G

[TDengine @TDengineDB]

Industrial systems generate massive streams of signals every second. But the moments that matter — anomalies, state changes, process events — are often hidden inside that data.

In this TDengine IDMP demo, we explore how analyses and event detection help transform raw time-series signals into meaningful operational events. It’s a look at how continuous data streams can be interpreted and turned into insights engineers can actually use.

https://t.co/mrLuI5zm6h

[TDengine @TDengineDB]

In this TDengine IDMP demo, we walk through a chemical manufacturing example and explore the platform’s range of visualization options for industrial data.

Operational data isn’t just numbers — it’s trends, states, and events unfolding across a process. This demo shows how IDMP helps make those patterns visible.

Watch now: https://t.co/UDdAUBRgeR

[TDengine @TDengineDB]

Modern steel manufacturing systems generate massive volumes of operational data every second.

This case study looks at how a legacy data stack was replaced with a TDengine-based architecture, simplifying the pipeline while supporting high-frequency ingestion (up to 250,000 writes/sec) and real-time analytics across production systems. A practical example of how modern industrial data platforms can handle both scale and speed in complex manufacturing environments.

Read the full article: https://t.co/o3zbETyCef

[TDengine @TDengineDB]

In this second TDengine IDMP demo, we walk through the platform’s visualization features, including how to build panels and dashboards to monitor assets, track key metrics, and explore operational data in real time.

Check it out at https://t.co/uDaOlguZzV

[TDengine @TDengineDB]

Event frames are one of the most powerful ideas in industrial data systems.

In this article, Jeff Tao explains why event-based analysis - long used in platforms like PI Vision - matters even more in the AI era. Industrial systems aren’t just streams of metrics; they’re sequences of operational events, and preserving that context is key to making data and AI truly useful.

Read the full article: https://t.co/c2l5masuDj

[TDengine @TDengineDB]

This short video walks through the fundamentals of TDengine IDMP, from the platform overview to the tree hierarchy for assets, navigating the interface, and defining attributes to contextualize industrial data.

A quick look at how operational data can be organized into meaningful structures that are easier to explore, analyze, and use.

https://t.co/X8k6jpbfIc

[TDengine @TDengineDB]

We’re excited to share that TDengine has reached 1,000,000 installations worldwide!

This milestone wouldn’t be possible without the incredible developers, customers, and partners who continue to build with us. Thank you to everyone in the TDengine community who made this possible.

[TDengine @TDengineDB]

In his latest article, Jeff Tao shares a perspective on what’s missing in many modern data platforms: context.

Using PI Vision as a reference point, he explores how visualization alone isn’t enough. Without semantic modeling and contextual understanding, data remains disconnected from real operational meaning.

It’s a thoughtful take on where industrial data platforms need to evolve next: https://t.co/alfVTlRkZ3

[TDengine @TDengineDB]

TDengine’s automated edge–cloud synchronization makes it easy to keep operational data aligned between edge and centralized nodes using subscription or query-based replication. With configurable rules and automatic recovery from network interruptions, TDengine delivers simpler architectures and real-time visibility across sites.

Learn how to implement edge-cloud sync at https://t.co/lEzEH7mcgj or watch the video at https://t.co/46V3a0IXPM

[TDengine @TDengineDB]

Excited to announce our new partnership with Ecubix!

Together, we're bringing modern, AI-ready industrial data platforms to organizations across power, pharma, and oil & gas. By combining Ecubix's deep domain expertise with TDengine's AI-powered data historian, we're helping teams move beyond legacy data infrastructure to unlock real-time, scalable operational intelligence.

Read the full article: https://t.co/D25yx7GCRD