things like hive let you host massive database on hadoop cluster there' things like spark sql that let you also do queries in very sql-like data warehouse-like manneron data warehouse that is actually distributed on hadoop cluster there are also distributed nosql data stores that can be queried using spark and mapreduce the idea is that instead of using monolithic database for data warehouseyou're instead using something built on top of hadoopor some sort of clusterthat can actually not only scale up the processing and querying of that databut also scale the transformation of that data as well once againyou first extract your raw databut then we're going to load it into the data warehouse system itself as is andthen use the power of the data warehousewhich might be built on hadoopto do that transformation as the third step then can query things together soit' very big projectvery big topic you knowdata warehousing is an entire discipline in and of itself we're going to talk about spark some more in this book very soonwhich is one way of handling this thing there' something called spark sql in particular that' relevant the overall concept here is that if you move from monolithic database built on oracle or mysql to one of these more modern distributed databases built on top of hadoopyou can take that transform stage and actually do that after you've loaded in the raw dataas opposed to before that can end up being simpler and more scalableand taking advantage of the power of large computing clusters that are available today that' etl versus eltthe legacy way of doing it with lot of clusters all over the place in cloud-based computing versus way that makes sense todaywhen we do have large clouds of computing available to us for transforming large datasets that' the concept etl is kind of the old school way of doing ityou transform bunch of data offline before importing it in and loading it into giant data warehousemonolithic database but with today' techniqueswith cloud-based databasesand hadoopand hiveand sparkand mapreduceyou can actually do it little bit more efficiently and take the power of cluster to actually do that transformation step after you've loaded the raw data into your data warehouse this is really changing the field and it' important that you know about it againthere' lot more to learn on the subjectso encourage you to explore more on this topic butthat' the basic conceptand now you know what people are talking about when they talk about etl versus elt