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.