The introduction of Industry 4.0, also known as the fourth industrial revolution, is transforming industrial operations. The technology of connected devices has brought exponential changes in the industrial world. The adoption of the IIoT application is on the rise but collecting huge data is useless without the ability to analyze it. An IoT-enabled plant or manufacturing unit can generate a lot of data but simply collecting data is not enough. To improve the industrial operations and make the right business decisions, the traditional relational databases are of little help. For sensor-based data, it’s essential to utilize databases that specialize in collecting, contextualizing, and analyzing data to help to make data-driven process decisions.

When it comes to plant-floor information, the use of the right database technology can make a big difference. This is because sensors generate tons of data and businesses need to decide where to collect that data and how to use it for improving operational performance. Typically, the industrial sector handles data collection with either a traditional relational database or with a plant data historian. So the question arises which is better for your industry? In this post, we’ll find the difference between the relational database and data historian to help you understand which one is the best option for your applications.

Relational Databases

A relational database is a type of database that organizes data into tables to identify and access data in relation to another piece of data in the database. It stores information and provides access to data points that are related to one another. The data is organized into tables with each row holding a record with a unique identifier known as a key and each column containing the attributes of the data. The relational model of this database helps in making relationships between data points. The relational database management systems use the SQL language (Structured Query Language) to access the database.

The traditional relational database architecture is widely used for many applications but it’s not the best solution for real-time operations that require fast data collection for analysis. This system is inefficient for the Industrial Internet of Things (IIoT) applications and time-series data. A relational database management system (RDBMS) can be used to store financial records, keep records on employees, and track inventory. The relational model is simple and easy to use but it has some drawbacks.

As compared to advance databases, RDBMS can be slow and more expensive. Setting and maintaining this database can be costly because it often requires hiring a programmer to create RDBMS using Structured Query Language (SQL) and a database administrator to maintain the database. It is also seen that entering more data than the system can accommodate may result in information loss. There’s always a possibility that some information may be lost or forgotten. As the volume of data increases, the relations between pieces of data become more complex.

Data Historian

A Data Historian is a software program designed to collect and store process data from a SCADA or automation system. This database program records and retrieves production and process data by time. Data Historian, also known as a Process Historian or Operational Historian, stores the information in a time series database that can be displayed in a trend or as tabular data over a time range. 

The database collects real-time data from process control systems, sensors, equipment, and other data sources. The time-stamped industrial data which is stored, processed, and analyzed by this non-relational SQL database helps in decision-making. Data historians are used across industries, such as oil and gas, manufacturing, pharmaceuticals, and many more. In the manufacturing industry, the Operational Historian can be used to track production processes and find information such as vibrations of a motor fan on a production line, valve position, speed of a conveyor, or pH levels for a water treatment plant. 

Data historian is not simply a time-series database. It serves as the link between plant operations and the business to get an accurate understanding of current production status or historical trends. It combines the functionality of a time series database with turnkey business solutions to increase product quality and consistency. This system allows correlating data over time, such as day shift vs. evening shift. A plant-wide data historian can help in improving performance by finding out production and performance status to make data-driven decisions.