Data infrastructure is a significant investment for industrial enterprises and not something that can be “ripped and replaced” on a whim. As your business grows, new sites are likely to come online using different platforms or protocols, making it difficult to obtain company-wide data. Different systems, data structures, and even units of measurement can pose serious challenges for operations teams. For that reason, it’s crucial that your data centralization solution supports a wide variety of data sources.
When assessing TDengine and InfluxDB in the context of industrial data sources, both databases offer capabilities for handling time-series data in industrial IoT environments, but with distinct strengths and trade-offs.
TDengine: Purpose-Built for Industrial Scenarios
TDengine is purpose-built for industrial applications and supports modern industrial protocols. With TDengine, you can easily aggregate industrial data from various sources, including but not limited to:
- Modern industrial protocols, such as MQTT and OPC (UA and DA)
- Data collection agents, such as Telegraf and collectd
- Traditional data historians, such as PI System and AVEVA Historian
- Traditional Relational databases, such as MySQL, Oracle, SQL Server, PostgreSQL, etc.
- Apache Kafka
- CSV files
TDengine is a zero-code platform that enables ETL (Extract, Transform, and Load) processes for industrial data sources with minimal configuration.
Unlike other time-series databases, TDengine is purpose-built for industrial scenarios. It stands out by offering built-in support for many industrial protocols and systems, eliminating the need for extensive middleware. It seamlessly ingests real-time and historical data from sources such as MQTT, OPC (UA and DA), PI System, Wonderware, and AVEVA Historian, ensuring compatibility across both legacy and modern platforms. Additionally, TDengine includes robust ETL (Extract, Transform, Load) capabilities that automatically clean and transform raw data as it flows into the system, ensuring high data quality without manual intervention.
Its zero-code platform simplifies configuration, allowing users to connect multiple data sources through an intuitive graphical interface (GUI) without writing code. TDengine’s flexible data ingestion supports CSV files, relational databases (MySQL, PostgreSQL, Oracle), and Apache Kafka, facilitating seamless data aggregation from diverse environments. Once data from these sources is aggregated into TDengine, you can easily integrate visualization and business intelligence tools, such as Seeq or Power BI, to build company-wide dashboards and reports. You can also connect with third-party AI tools for advanced analysis. Your applications and algorithms will have real-time access to all data, enabling global insights and operational efficiency without the need for custom code or manual operations. Whether you’re working with real-time or historical data, TDengine provides a seamless integration experience, enabling businesses to unlock the full potential of their data across diverse systems and platforms.
InfluxDB: Versatile but Dependent on Telegraf
InfluxDB is primarily designed for managing time-series data from a variety of sources, with strong native support for MQTT and extensible capabilities for integrating with industrial systems like OPC, PI System, and AVEVA Historian through additional middleware or configurations. Its seamless MQTT integration allows users to subscribe directly to topics and store incoming messages as time-series data using the Native Collectors feature, making it well-suited for IoT applications by eliminating the need for extra middleware.
However, while InfluxDB is powerful, it heavily depends on Telegraf for most of its data ingestion processes. Telegraf serves as a versatile data collector with numerous plugins, supporting data sources such as MQTT, HTTP APIs, and relational databases like MySQL. Yet, when it comes to key industrial protocols like OPC, PI System, and AVEVA Historian, additional configurations and middleware are often necessary to transform and ingest the data into InfluxDB. This reliance on intermediary tools introduces operational overhead and increases deployment complexity, particularly in environments that require reliable, real-time data flows.
Without native support for many industrial protocols, organizations must manage multiple components to maintain smooth operations, making deployments less seamless compared to solutions with built-in protocol support. While Telegraf expands InfluxDB’s reach across various data systems, it requires monitoring and managing additional infrastructure, which can slow down deployment and complicate maintenance. This dependency means that aligning configurations across platforms becomes critical, and the time and effort required for these setups can limit InfluxDB’s out-of-the-box usability in complex industrial environments.
Conclusion
Both TDengine and InfluxDB offer powerful solutions for time-series data management, but they cater to slightly different needs. TDengine excels in industrial environments with its out-of-the-box support for key protocols, zero-code platform, and seamless integration with legacy and modern systems. InfluxDB, while highly capable and popular in IoT and monitoring applications, requires more effort to deploy and maintain due to its reliance on Telegraf for many data ingestion tasks.
For businesses seeking a streamlined, low-maintenance solution with broad protocol support, TDengine may offer a more efficient path. In conclusion, TDengine presents a more robust solution for handling industrial data sources compared to InfluxDB. Its native support for industrial protocols, superior ingestion capabilities, and optimized performance metrics make it particularly well-suited for large-scale industrial IoT deployments. In contrast, while InfluxDB remains a viable option for IT-centric applications, it may require additional configuration and complexity to meet the demands of high-volume industrial environments.
