Ashish Kumar and Jorge Villamariona take us through data lakes and data catalogs:
Any data lake design should incorporate a metadata storage strategy to enable business users to search, locate and learn about the datasets that are available in the lake. While traditional data warehousing stores a fixed and static set of meaningful data definitions and characteristics within the relational storage layer, data lake storage is intended to support the application of schema at read time with flexibility. However, this means that a separate storage layer is required to house cataloging metadata that represents technical and business meaning. While organizations sometimes simply accumulate content in a data lake without a metadata layer, this is a recipe for an unmanageable data swamp instead of a useful data lake. There are a wide range of approaches and solutions to ensure that appropriate metadata is created and maintained. Here are some important principles and patterns to keep in mind. Single data set can have multiple metadata layers dependent on use cases. e.g. Hive Metastore, Apache Glue etc. Same data can be exported to some NoSQL database which would have different schema.
Having a bunch of data isn’t helpful if you don’t know where it is, how it’s formatted, or anything else about the data.