The Fourth Industrial Revolution, commonly known as IR4.0, has ushered in profound transformations in the landscape of industrial operations and manufacturing. Digital technologies and data derived from sensors are driving a wide array of innovations, spanning from advanced analytics and machine learning to the realms of augmented and virtual reality models.
Handling sensor-based data poses a unique challenge for conventional relational databases, which is why data historians were originally conceived in the latter part of the 1980s. They were specifically designed for integration with industrial automation systems like SCADA (supervisory control and data acquisition). Initially, their primary application was within the process manufacturing sector, encompassing industries such as oil and gas, chemicals, pharmaceuticals, pipelines, and refining.
This Historian system was developed as an ecosystem that provided a comprehensive solution, ranging from data interface software to data storage and data visualization. The industry 4.0 revolution has spurred automation in manufacturing, leveraging smart sensors and IoT devices to capture real-time data from the field. Furthermore, it has seen the increased utilization of Artificial Intelligence and Machine Learning for predictive analytics and decision support, both of which are data-hungry applications. So, with IR4.0 development what are the options available to cater requirements for real time data ingestion.