Time Series DB vs. Historian in the Unified Namespace (UNS)

In the field of Industrial IoT (IIoT) and Industry 4.0, the Unified Namespace (UNS) has emerged as a central architecture that centralizes real-time data streams within a company. Within this framework, two primary systems are often considered for data storage and analysis. Time Series DB (TSDBs) vs Historian. While both are used to manage timestamp data, they differ significantly in their functions, integration capabilities and roles within the US. There is no one-size-fits-all solution; the best solution depends on individual needs and priorities.

Understanding the Unified Namespace (UNS)

The UNS acts as a central data structure that organizes and makes accessible all real-time data from different sources – such as PLCs, sensors and MES systems – via standardized protocols such as MQTT. This architecture facilitates the seamless flow of data throughout the company and promotes interoperability and real-time analysis. Find out more here.

Historians: Traditional solutions for time series data

Historians, (like Canary Labs) have long been an integral part of industrial environments. They are designed for the efficient collection, storage and retrieval of time series data from operational technologies.

Pros:

• Mature, reliable and designed for industrial use (for OT People): Historians have a long track record in industrial environments and offer proven reliability and stability for critical operations. They have been designed with the requirements and constraints of OT environments in mind.
• Compliance-capable (legal standards such as 21 CFR Part 11): These systems are often equipped with features that support compliance with industry-specific regulations and simplify the audit process. This built-in compliance is critical in highly regulated industries.
• Native integration with OT systems (SCADA, PLC, either as part of the system or as a standalone system): Historians are designed to integrate seamlessly with common industrial control systems and enable the collection of real-time data from PLC and SCADA systems. This tight integration simplifies deployment and ensures compatibility with existing infrastructure.
• Strong data compression and long-term storage: These systems are characterized by the efficient storage of large amounts of historical data, enabling trend analyses and long-term performance monitoring. Effective compression ensures that large volumes of data remain manageable.
• Event tracking and audit trails: Historians provide detailed event tracking and audit trails that provide valuable insight into system events and operational anomalies. These audit trails are essential for troubleshooting, root cause analysis and regulatory compliance.

The challenges:

Data historians offer clear advantages. However, in the context of Industry 4.0, their closed nature raises the question: what if data is to be used more widely?

• Less flexible for custom analytics and data science: Historians can be less adaptable to advanced analytics techniques that require specialized data transformations or integration with modern data science tools. This limited flexibility can hinder innovation and advanced data-driven decision making.
• Licensing costs can be high: Due to their specialized nature, licensing costs for historians can be significant, especially for large-scale implementations. The high cost can be a barrier to entry for some companies.
• Scaling and exporting large data sets can be complex: Scaling Historians to adapt to growing data volumes and exporting large datasets for external analysis can be a complex and time-consuming process. This complexity can limit the ability to adapt to changing data requirements.
• Slower for ad-hoc queries or high-frequency data retrieval: Your architecture may not be optimized for ad-hoc queries or the fast retrieval of high-frequency data required for real-time analysis. This can affect the responsiveness of important applications.

Time Series Databases (TSDBs): Modern alternatives

TSDBs such as TimescaleDB and InfluxDB are specialized databases. They are semantically structured and are particularly suitable for high-speed environments. Data is primarily appended there as new entries instead of changing existing data records.

Pros:

• Powerful, fast reads and writes for massive data streams: These databases are specifically designed for efficient ingest and retrieval of time-stamped data and are ideal for high-volume, high-speed environments. This performance is critical for real-time monitoring and analysis.
• Horizontal scaling, cloud-friendly: Time series databases can be easily scaled horizontally across multiple nodes, making them well-suited for cloud deployments and growing data volumes. Cloud-native designs facilitate deployment, management and scaling.
• Excellent for analytics, ML and custom dashboards (for IT staff): They offer powerful features for performing complex analytics, building machine learning models and creating custom dashboards to visualize data insights. Their flexibility is a key strength for data exploration and predictive analytics.
• Open source or lower license costs: Many time series databases are open source or offer cheaper licensing models compared to traditional historians. This can significantly reduce the total cost of ownership.
  Easy integration with modern tools (Grafana, Python, Spark, etc.): They offer seamless integration with a wide range of modern data tools and programming languages, allowing for greater flexibility in data analysis and application development. This broad compatibility promotes innovation and integration into the existing IT infrastructure.

Challenges:

• Lack of native OT compliance features: Time series databases may not have built-in features to support compliance with industry-specific regulations such as 21 CFR Part 11, and implementing these features often requires additional effort and expertise.
• Requires additional tools for context and event enrichment: Additional tools and processes are usually required to add contextual information and enrich event data for more meaningful analysis. This can increase complexity and the need for expertise.
• Not always optimized for long-term data storage: Your architecture may not be optimized for the storage and efficient retrieval of very long-term historical data. Long-term archiving solutions may be required.
• Data quality and validation are highly dependent on external processes: Data quality and validation depend on external processes and tools and require careful implementation to ensure data accuracy. This external dependency places additional burdens and responsibilities on the data pipeline.

Time series databases vs. historians

Conclusion

Both historians and time series databases offer unique strengths within a unified namespace for the industrial IoT. Historians offer proven reliability, compliance features and OT system integration, making them well suited for traditional industrial environments. Time series databases are particularly good at processing large amounts of data. They also enable advanced analytics and offer flexible integration with modern IT tools. The choice depends on how well compliance, OT integration, scalability, analytics and cost are balanced in Industry 4.0.