Traditionally data warehouses do not contain today’s data.
They are usually loaded with data via an Extract, Transform and Load (ETL) process from operational systems on a periodic basis, often nightly, but sometimes even weekly. In any case, they are a window on the past.
With the ever-growing pace of business today, are these history-based analytical data sources providing the right insights when they are most needed?
In this blog, we look at the key differences, or scenarios where one might choose a data warehouse solution versus using real-time data sources to provide operational analytics. Let’s first look at data warehouses. They are designed to answer exactly the types of questions that users would like to pose against real-time data. They are able to analyse particularly large volumes of consolidated data and be sliced by any given dimension built into your warehouse such as time, customer, sales representative, region, product class or category to name just a few.
As data is pre-populated and aggregated, performance is often much faster compared to real-time sources where calculations and data retrieval are performed off transactional sources where the data originated. Generally non-real time, or history-based BI data will be best suited to the following needs: