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Vendor Lock-In and Data Freedom: TDengine vs. PI System

Jeff Tao

November 6, 2025 /

In the world of industrial data, ownership and portability define how much control you truly have. Once your time-series data is trapped in a proprietary ecosystem, every migration, integration, or upgrade becomes a challenge.

Two Philosophies on Data Ownership

TDengine is built on open standards and interoperability. It uses standard SQL, supports standard interfaces like JDBC and ODBC, includes client libraries for programming languages such as Python and Node.js, and exports data in open formats like Parquet. Whether you deploy on-premises or in the cloud, you can integrate TDengine with analytics, BI, visualization, and other tools without proprietary adapters or license restrictions.

PI System, by contrast, is rooted in a closed, proprietary model. Data resides in specialized file structures and is accessed through AVEVA-specific APIs or connectors. While integrations exist, they often require paid interfaces, complex configuration, and strict version alignment. Exporting large datasets or connecting with modern platforms can be slow, costly, and inflexible.

Integration and Extensibility

TDengine’s open interfaces make integration effortless. You can stream data from Kafka, visualize it with Grafana, connect directly from a Python app — whatever you need to get the job done. All these integrations are included in the base TDengine package and do not cost extra. TDengine fits into your chosen data stack, and your data remains fully under your control.

TDengine at the center of its open ecosystem, ingesting data from a variety of souces, sharing data with authorized third-party apps, and distributing data to consumers and applications

Furthermore, TDengine provides flexible, built-in tools to export data into open formats such as Apache Parquet or CSV, ensuring that information collected and stored within TDengine can be easily shared with other analytics systems or long-term archives. Parquet support in particular allows enterprises to move large volumes of time-series data efficiently into data lakes or object storage for batch analytics, machine learning, or compliance retention. There are no proprietary file types or conversion utilities to manage, and no additional licensing required to extract your own data. In essence, TDengine gives you complete freedom to store, process, and move your time-series data wherever it creates the most value.

PI System’s extensibility depends heavily on its own ecosystem. The AF SDK and Web API offer rich functionality, but they keep you anchored to the same vendor stack. To connect with external cloud services or data platforms, many users rely on AVEVA CONNECT data services, third-party middleware, or even custom scripts, which introduces additional cost and operational overhead.

More importantly, PI System provides little support for customers who want to migrate their data elsewhere. Its proprietary data structures, tightly coupled configuration files, and limited export capabilities make large-scale data extraction difficult and time-consuming. There are no native tools for bulk exporting to open formats, nor are there official pathways for integrating with modern cloud data warehouses or AI platforms without additional software layers. As a result, organizations that have accumulated years of operational data inside PI System often find themselves effectively locked in, facing high migration costs and significant engineering effort if they ever choose to move to another solution.

The Hidden Cost of Lock-In

Vendor lock-in isn’t a technical constraint but a strategic one. Every proprietary dependency increases your reliance on existing vendors and decreases your flexibility to innovate or optimize costs as your business evolves. Want to try out a cutting-edge app or data service? If your vendor doesn’t provide a connector, you’re essentially out of luck. Once your data is locked inside a closed system, even simple changes, like adopting a new analytics tool or shifting to a cloud platform, can become time-consuming and expensive at best and impossible at worst. In the end, what should be a technology decision often turns into a business negotiation.

With TDengine, you avoid those limits entirely. Your data remains open, portable, and interoperable from day one. You can easily export data from TDengine to external systems if you decide to migrate in the future. And for organizations that already rely on PI System, TDengine even provides a built-in PI System connector, allowing you to access and analyze PI data directly within TDengine’s unified environment.

Conclusion

TDengine’s mission is simple: to make data accessible, affordable, and valuable. That means no hidden barriers, no closed formats, and no vendor lock-in. You decide where your data lives, how it’s used, and who gets access to it.

PI System has long been trusted across industries, but its tightly coupled ecosystem often makes data freedom difficult to achieve. If your future depends on openness and agility, TDengine gives you the power to keep your options open and your data fully in your hands.

  • Jeff Tao

    With over three decades of hands-on experience in software development, Jeff has had the privilege of spearheading numerous ventures and initiatives in the tech realm. His passion for open source, technology, and innovation has been the driving force behind his journey.

    As one of the core developers of TDengine, he is deeply committed to pushing the boundaries of time series data platforms. His mission is crystal clear: to architect a high performance, scalable solution in this space and make it accessible, valuable and affordable for everyone, from individual developers and startups to industry giants.