Over the last several years there has been a lot of discussion about data lakes where a data lak e was originally defined as a central data store on Hadoop holding raw data . Since then, new configurations of a data lake have emerged such as cloud storage and also a logical data lake made up of multiple data stores spread across data centre(s) and cloud(s) where data is captured in many organisa tions today. Data lakes were initially seen as a place where raw data could be brought together to support data science. This is a single purpos e use case. However, for many organisations, bringing together data just fo r data science is way too restrictive. All that data is way too valua ble to just set aside for a very small number of data scientists when there are many other purposes such a valuable collection of data could be used f or. For example, it could be used to stage and process data to build a data warehouse. In addition, it could be used to build and maintain master data management (MDM) systems, or reference data management (RDM) systems. It c ould also be used to build a single customer view for marketing, all of whi ch are in addition to data science. In that sense the data lake could be mu lti-purpose. It is this realisation that has opened up the idea that rather than build separate systems like data warehouses, MDM systems etc in silos , they could in fact turn the data lake into huge engine room data hub to p roduce all data assets needed to create a data driven enterprise. This sess ion looks at this possibility and shows how companies can create a multi-pu rpose data lake to build reusable trusted data and analytical assets to ena ble rapid delivery of data warehouses, data marts, MDM, RDM, single custome r view and data science. It not only looks at how companies can create thes e assets but also how they can publish them in a catalog to make them finda ble and how you can link these assets together as components to rapidly bui ld data and analytical pipelines for competitive advantage
\n\n What is a data lake and how have they evolved?\n Why create a data lake? - The data science use case\n Limitations of a single purpose data lake\n The benefits of a multi-purpose data lake\n What is needed to build a multi-purpose data lake\n Key technologies in a multi-purpose data lake\n