For data lakes, as with any valuable enterprise data set, architecture is a requirement. Without the design patterns, organization, and standards imposed by architecture, a data set will be hard to maintain, scale, optimize, govern, access, and leverage for organizational advantage. Hence, a good architectural design is key to achieving business value and a return on investment (ROI) from a data lake.

Before diving into data lake architectures, let’s review TDWI’s definitions of architecture and how multiple architectures work together in data-driven applications such as analytics, data warehousing, and data lakes.