The last four decades has seen an evolution of data platforms. It started off with building enterprise data warehouses and data marts, to data lakes. Data lakes also evolved from being implemented on-premises, to implementation over the cloud. There are subtle differences and many similarities in the way these platforms have evolved. ETL (Extract Transformation and Load) came into being with the advent of data warehousing, while ELT (Extract Load Transform) became popular with MPPs (Massive Parallel Processing) and data lakes. Data warehouse, in conjunction with data marts, aims at supporting data analysts for operational reporting and data mining needs. Whereas, data lakes are the platforms for data scientists who aim to discover patterns in the data and put it to use.
Among these differences, there lies a similarity, that is, having it all built around a central data store, with a common development paradigm – Data extraction from many sources; transformation and loading to the central data store. Download whitepaper to continue reading.