Many companies are now changing their data architecture to create a central data lake on cloud storage or a logical data lake made up of multiple data stores to ingest and process data for use in multiple analytical environments. Data lakes were initially seen as a place where raw data could be brought together to support data science. However, many organizations see this as too restrictive when there are many other purposes that a valuable collection of data could be used for – for instance, in a data warehouse. This session shows how companies can create a multi-purpose data lake to enable rapid delivery of data warehouses, data marts, MDM, RDM, Customer Data Platform, and data science. It shows how data assets can be published in a catalog to make them findable and how you can link these assets together as components to rapidly build data and analytical pipelines for competitive advantage.

We will cover: 

• What a data lake is and how have they evolved
• Why create a data lake? The data science use case
• Limitations of a single-purpose data lake
• The benefits of a multi-purpose data lake
• What is needed to build a multi-purpose data lake
• Key technologies in a multi-purpose data lake
  • The data management platform/data fabric
  • The data catalog
  • Machine learning and advanced analytics
  • Data virtualization
• Building trusted reusable data and analytical assets using pipelines
• From data lake to multi-purpose logical data hub – publishing assets to a catalog to fuel reuse
• Orchestrating ready-made data and analytical assets in pipelines to rapidly deliver high-value insights for competitive advantage