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Beyond the Traditional Data Historian

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.

Rtdip generative ai sql agent

RTDIP Generative AI SQL Agent


Generative AI is changing how we think about data, particularly the knowledge it can unlock from unstructured data that simply wasn't possible before. However, it's also peaked our curiosity about structured data - Can Generative AI also query structured data? Could it query time series data to answer questions such as:

What was the average actual power generated by Turbine 1 at ACME Wind Farm on 6 May?"

Rtdip ingestion pipelines

RTDIP Ingestion Pipeline Framework


RTDIP has been built to simplify ingesting and querying time series data. One of the most anticipated features of the Real Time Data Ingestion Platform for 2023 is the ability to create streaming and batch ingestion pipelines according to requirements of the source of the data and needs of the data consumer. Of equal importance is the need to query this data and an article that focuses on egress will follow in due course.