Let's take a quick look at how data management has evolved. How Business Requirements Shape Database Technologies Instead, it's necessary to add several layers of transformations, enrichments, and business rules to optimize cost and performance. That doesn't mean you store the data in raw form. Case-based data transformation could be more economically viable as more ad-hoc queries and analyses pop up every day.
Applying rigid data transformation rules and making data available for your teams through a data warehouse may not fully address your business's evolving and exploratory data integration needs.ĭepending on the volume of data your organization produces and the rate at which it's generated, processing data without knowing the consumption patterns could prove to be costly. This trend reshapes how data is ingested and transformed across lines of business as well as by departmental users, data analysts, and C-level executives. It has driven businesses to integrate extract, load, and transform (ELT) tools with Medallion architecture. As the world takes a multi-layered approach to data storage, there is a shift in how organizations transform data.
