Powering a Data Hub at Otto Group BI with Schedoscope

In order to build data services or advanced machine learning models, organizations must integrate large amounts of information from diverse sources. As a central place to consolidate as many data sources as possible we often find what is fashionably called a data lake. Building a data lake usually starts by collecting as much data in raw form as possible. The idea is to give data scientists simple access to all available data so that they can combine information in ways not yet anticipated. Hadoop is the preferred choice for such a system because it is able to store vast amounts of data in a cost-efficient manner and is largely agnostic to structure. Weiterlesen