Data sharing plays a crucial role in driving digital transformation, fostering innovation, and improving overall efficiency in industrial operations. However, traditional data historians like PI System are typically not equipped to share data in a way that meets the needs of modern Industrial IoT applications. Unlike these products, TDengine delivers simple and secure data sharing that makes collaboration on industrial data as easy as sharing Google Docs.
A New Way to Share Time-Series Data
Traditional data sharing in industrial systems has often been clumsy. Exporting raw files, moving entire datasets between systems, or relying on third-party middleware created complexity and security concerns. TDengine takes a different approach. Instead of copying data back and forth, its built-in data subscription component to let you define exactly what should be shared using SQL, and then pushes only that stream to the right consumers. This means engineers can publish an aggregated metric or a filtered slice of sensor data, while managers and partners see only what they need.
Sharing is controlled with fine-grained permissions that work across organizational boundaries. An entire database can be replicated to another cloud instance with a single token, or an individual stream can be shared with an external partner who authenticates via email invitation. Whether for internal teams or outside collaborators, TDengine ensures that access is simple, secure, and auditable.
Kafka-Like Subscriptions Without the Overhead
One of the strengths of TDengine is that it brings Kafka-like subscription patterns directly into the database. Instead of standing up and maintaining a Kafka cluster, you can create a subscription with nothing more than a SQL query. The result is a virtual “topic” defined by your query logic, whether that’s raw sensor feeds, aggregated values, or filtered results.
Consumers can connect through familiar APIs and read the data in real time, complete with acknowledgments and consumer groups for reliable delivery. Replay is also supported: you can roll back to a specific time range and process the stream again, a powerful tool for debugging or re-running analytics. This approach delivers the event-streaming flexibility people expect from Kafka, but without the cost and operational burden of managing a separate messaging system.
Embracing MQTT for Industrial IoT
While Kafka patterns are popular in IT systems, industrial and IoT environments often rely on MQTT for lightweight, device-to-cloud communication. TDengine supports this natively as well. By enabling an MQTT broker node, the database itself can act as a publisher, pushing updates directly to subscribing devices or applications. These subscriptions follow MQTT 5.0 standards, with shared subscription groups and QoS controls, making them highly compatible with existing IIoT deployments.
Unifying the Ecosystem
By supporting both Kafka-like and MQTT-based data subscription, TDengine bridges two worlds. IT teams gain the reliability and flexibility of a message bus without deploying extra infrastructure, while OT teams keep their familiar MQTT workflows and integrate seamlessly with the database. Add in secure sharing and replication between cloud instances, and TDengine becomes more than just a time-series database — it is a hub for industrial data distribution.
The result is a system where data can move freely, securely, and in real time, whether you are streaming millions of sensor readings per second into analytics dashboards, replicating operational metrics across regions, or feeding machine learning pipelines.
Simple and Secure Data Sharing in TDengine Cloud
TDengine Cloud makes sharing data as easy as sending an email, while keeping security front and center. You can invite collaborators inside or outside your organization, assign roles or user groups, and set expiration times or encryption for extra protection. Multi-level controls at the organization, instance, and database level let large teams fine-tune access with precision.
For broader collaboration, entire instances or databases can be shared, or replicated across regions with a secure token. For more targeted needs, authorized topics let you use SQL to define exactly what data is streamed — aggregated, filtered, or transformed — and share it with selected users without exposing the rest of your system.
Conclusion
With TDengine, data sharing is no longer a bolt-on feature but a core capability of the platform. Whether through secure email invitations in TDengine Cloud, instance and database replication across regions, Kafka-like or MQTT-based data subscription, TDengine ensures that data flows where it’s needed in real time, and with full control. This foundation not only simplifies collaboration but also prepares organizations to put their time-series data to work for analytics, automation, and AI.
