To get around the classic data warehouse limitations, many enterprises have implemented unstructured data  lakes, often built around Hadoop data stores, that promise an alternative to data processing. 

These data lakes serve to store large amounts of semi-structured and unstructured data. Their flexibility led to the  idea of “schema-on-read,” which in turn has led to the increasing need for intense data preparation. The need to  prepare every piece of data with uncommon skill sets has shifted autonomy away from data analysts and data  scientists, who now must jump through hoops to access the data they need.  The Hadoop ecosystem has ultimately failed to deliver the flexible, interactive access to data that it promised, and  has discouraged data professionals by hiding data behind programming APIs and a wide slew of difficult-to-use,  inadequate tools.