The benefit of ad hoc analysis and how a data lake enables it
Before the start of the data lake pattern, organizations used to offload their data into a data warehouse for analysis. This involved creating an Extraction, Transformation, and Load (ETL) pipe. Creating ETL pipes, moving the data into a warehouse, and creating reports take a substantial amount of time and resource investment. By the time all of this has finished, the requirements will have changed because of the change in the business over a period of time. Sometimes, business users discovered that they didn’t get what they ordered and that there was a gap in requirement and implementation.
For example, a business user could request sales data, resulting in the IT team moving the sales data into the warehouse. However, the sales data in the warehouse might not be of the grain that the business user needs or does not include the sales data from all the sources of sales information. All of this involves a massive...
