Before we dive deep into the subject, let’s clarify operational analytics briefly. Operational analytics is a subset of data analytics that aims to improve business operations. Business operations are the everyday activities businesses participate in to improve their value and generate a profit.
The primary distinction between operational analytics and other forms of analytics is that operational analytics is analytics on the fly, which means that data emerging from different segments of an organization is analyzed in real-time to feed back into the organization’s immediate decision-making. Some people refer to operational analytics as continuous analytics, which is another way of highlighting the continuous digital feedback loop that can exist between different components of an organization.
ETL, which stands for extract, transformation, and load, is a traditional data integration approach that takes data from several data sources, transforms it, and stores it in a centralized data repository. This centralized data repository might be a data warehouse or a data lake, and it would serve as the only reliable source of truth.
In this ETL-based method, data goes through staging and integration phases before arriving at either a data warehouse or a data lake as the final destination.
What is reverse ETL?
We switched from ETL to ELT (Extract, Load, and Transform) as data volumes increased and we needed a faster way to load data into a data warehouse or data lake.