When you are struggling to build the Next Gen Data Platform, it is mostly because you are still recovering from past failures of Data Warehouse or building the Next Gen Data Platform..
Mostly enterprises have very rich domains and rich data sets. However the state of the data is often relatively unpredictable and poor in quality. Also companies struggle to find out what the definition of a Data Lake is..
Let me jump into the evolving phases of the Data Lake.
A data lake once was a place that aggregates raw data from all sorts of sources from all corners of the data organization into one place allowing analytical usage and data scientists to dive in.
After that it moved into a single place that pipelines to cleanse and transform and provides different views and different access on that data, which also exists as part of the pipeline of that data.
Currently it is more of a single, centralized and kind of monolithic data platform to provide data for a variety of Use Cases. Whether this is BI, Analytical or ML based, accumulating data from all the domains and sources in the organization.
