Long-Term Data Retention in IIoT with Time Series Databases

December 16, 2024

Data Retention for Long-Term IIoT Monitoring Using Time Series Databases | SPONSORED

Traditional industries have evolved by implementing many Industrial IoT practices, where we can get data from mechanical devices and sensors. Such real-time data monitoring helps perform better analysis, predict equipment failure, and reduce operating costs. However, IIoT creates enormous volumes of data that must be retained and controlled over long periods. Managing large volumes of data creates performance, scalability, and retention challenges. Ordinary relational databases or data historians can’t handle the demands of high-volume time series data, so you may need time series databases (TSDBs). TSDBs offer a sound architecture that accommodates the ceaseless stream of time-stamped data.

This post will examine long-term data retention, its challenges and best practices, and how to leverage a TSDB for long-term IIoT data retention.

The importance of long-term data retention in IIoT

Why retain data long-term?

Here are some reasons to retain data long-term:

Historical Analysis: Long-term data storage is beneficial since it enables periodic analysis of business trends. It is critical for enhancing operations, refining activities, and applying preventive or predictive maintenance, all to ensure that a facility avoids hardware breakdowns.

Compliance and Auditing: In the energy, healthcare, and manufacturing industries, keeping operations data for a specific duration is legally mandated. This is especially useful in audits, safety reports, and confirming a system’s operational credibility over time.

Machine Learning and AI: Machine learning and artificial intelligence require long-term data to train their models. These models use a lot of historical data to predict future events like equipment failure or process inefficiency.

The challenges of long-term data retention

Volume of Data: IIoT generates real-time big data from several thousands of connected devices and sensors over a long period. Storing this data becomes expensive in terms of hardware and personnel and requires specialized storage equipment.

High Cardinality Data: Various implementations of IIoT produce data with high cardinality, for example, to identify specific machines, geographical areas, or definite parameters of production processes. Handling and indexing data is not simple, as conventional archiving models may need to help accommodate numerous combinations within the data.

Performance: As data is retained for extended periods, ensuring fast and reliable access to historical data for real-time analysis becomes increasingly difficult. Ensuring the system can perform high-speed queries and provide actionable insights even with large datasets is challenging.

Best practices for long-term data retention in IIoT

Set appropriate retention periods

Begin by setting data retention periods, which regulatory requirements, operational demands, and analytical requirements can determine. Some businesses, like healthcare and manufacturing, may need to retain data for several years to meet regulations.

Not all data created in IIoT surroundings is equally helpful. Focus on data that will continue to provide the most value. This can be data related to the health of the machines or how efficiently they are running. Storing only essential data reduces storage demands and enhances performance.

Automate data retention and archival

Choose a TSDB for specific high cardinality logs and metrics, such as InfluxDB, which lets users expire data at configurable levels, such as hourly, daily, or monthly. These policies help remove unnecessary data that takes up space and help check compliance with retention laws. You can also downsample high-resolution data to retain only necessary historical data granularity. This practice reduces storage consumption while preserving trends needed for long-term analysis.

Optimize query performance

Implement indexing and compression techniques within the TSDB to enhance query speed and efficiency, particularly when retrieving recent and historical data. Monitor query performance regularly and adjust optimization settings as datasets expand. Proactive maintenance helps prevent performance bottlenecks, ensuring seamless access to valuable insights regardless of dataset size.

Data retention strategies for IIoT monitoring

Define retention policies

Establish clear guidelines on data retention duration, including the periods for keeping high-resolution data, the intervals for downsampling, and the timing for data expiration or archival.

Retention policies in InfluxDB are highly flexible to match the conditions characteristic of different industries. For instance, all sensor data generated at a high frequency may be stored at full resolution for days or weeks to capture short-term variations, while data generated in the distant past may be stored at a lower frequency and resolution.

Multi-tier storage with InfluxDB

InfluxDB organizes data into a multi-tier model, distinguishing between “hot” and “cold” data. Hot storage parks the latest fresh data in memory, allowing real-time analytics and rapid access. After a certain period of time, data is compacted and moved to object storage, cutting the storage costs significantly but still permitting queries with reasonable performance.

Hybrid cloud and on-premises storage options

InfluxDB enables organizations to store data on both local and cloud platforms. It combines the best characteristics of on-premises, as applications that need up-to-date data can operate on this data in-house. In contrast, applications that require massive data storage can store the data in-house but keep archives in the cloud for affordable storage.

Organizations can retain large datasets by leveraging cloud storage for historical data without overloading the local infrastructure. At the same time, maintaining critical real-time data on-premises ensures that external network dependencies do not impact latency-sensitive applications.

Time series data in IIoT: characteristics and storage needs

Characteristics of time series data

Time series data includes data collected frequently from several connected sensors, machines, or devices. Every reading, taken per second or millisecond, is processed and aligned with a timestamp corresponding to the sampling time. This is quite useful in IIoT since even small changes in the order of microseconds can be very relevant in determining equipment health and status.

Time series data includes temperature, pressure, vibration, energy consumption, power, status indicators, and humidity. They offer a current understanding of work processes and help monitor and analyze equipment status, performance, and potential failures.

Unique storage requirements for time series data

Efficient Compression: Given the high volume of time-stamped data, efficient storage is essential. Compression techniques help to reduce storage costs without compromising data precision, ensuring that long-term data remains accessible for historical analysis.

Fast Query Performance: Time series databases must deliver fast query speeds to support real-time insights and decision-making, even when managing extensive datasets. High-performance querying enables real-time monitoring and quick retrieval of historical data for analysis.

Data Downsampling: As data ages, it is often downsampled or reduced in resolution to save storage space. Downsampling maintains essential trends while reducing the storage burden, allowing organizations to retain historical data without overwhelming storage resources.

InfluxDB: a scalable solution for long-term IIoT data retention

Overview of InfluxDB for time series data

InfluxDB is a fast-time series database optimized for handling real-time data flows that are familiar to IIoT networks. Developed with scalability and efficiency as key pillars, InfluxDB works with the type of data generated from the IIoT environment and addresses some complexities, such as high-cardinality data, where much of the data generated can be identifiable to the device, sensor, or machine involved. Unlike most systems, InfluxDB allows unlimited cardinality to query millions of distinct sources and metrics without decreasing performance.

InfluxDB stands out as a powerful, scalable solution explicitly tailored to the requirements of time series data in IIoT applications. With built-in support for efficient compression, high-speed querying, and data downsampling, InfluxDB addresses the challenges of storing and accessing time series data.

Published data about the usage of InfluxDB for large-scale IIoT data underscore its applicability for handling these vast databases. It has been proven to help industries address operational needs promptly and make decisions based on current and historical data.

InfluxDB’s storage capabilities: Apache Parquet for long-term retention

InfluxDB stores historical IIoT data as highly compressed parquet files, optimizing storage by significantly reducing data size.

Apache Parquet’s columnar storage structure is suitable for time series data, as it allows for efficient data retrieval and faster analytical querying across large datasets. Parquet optimizes the speed and efficiency of queries by organizing data by columns rather than rows. This makes it possible to scan only relevant data rather than entire rows.

Conclusion

Long-term data retention is critical in IIoT environments as a foundation for historical analysis, regulatory compliance, and predictive maintenance. Organizations can uncover patterns, maintain operational transparency, and proactively address potential equipment failures by retaining and analyzing extensive time series data. However, storing vast volumes of IIoT data for extended periods can be costly and challenging without the proper infrastructure. InfluxDB stands out as a scalable solution for IIoT data management, leveraging Apache Parquet for highly compressed, columnar data storage. Integrate InfluxDB to your IIoT infrastructure to build a streamlined data retention strategy that optimizes costs, enhances performance, and meets compliance requirements, paving the way for smarter, data-driven industrial operations.

Data Retention for Long-Term IIoT Monitoring Using Time Series Databases | SPONSORED