Interactive Data Science and BI on the Hadoop Data Lake

For most companies have deployed data lake strategies the original motivation was to replatform their analytics onto a more modern architecture, taking advantage of commodity infrastructure and open source software in hopes of processing greater volumes of data at a lower cost. However, most projects have failed to deliver on this vision, and today companies find their data lakes are a complement to the traditional data warehouse and data mart rather than a replacement.

What went wrong?

In this whitepaper, we cover:

  • Key challenges with complexity, performance, and data quality on a datalake
  • Data-as-a-Service and how DaaS can resolves these key challenges
  • Common usecases for Data-as-a-Service

See how you can get started on transforming your data lake strategy.