451 Group’s research indicates 57% of the enterprises currently using a data lake cite improved business agility as a benefit. However, the previous approaches to data access and query processing involved creating and managing many disconnected copies of data, making it challenging to generate insights from a data lake. Aligning organizational needs with data lake engine requirements ensures that you drive the desired business outcomes from your BI and analytics projects.

Read this exec brief to learn:

  • Twelve key considerations for evaluating a data lake engine for BI and analytics
  • Considerations include support for data lake BI use cases, data lake analytics, open technologies, cloud migration, and financial governance among others
  • Sample questions to add to your RFI for each of the twelve considerations