The simple way to understand the utility of a data lake is "S3 is phenomenally cheap". Some people treat this as an excuse to throw whatever they want into it,

The simple way to understand the utility of a data lake is "S3 is phenomenally cheap". Some people treat this as an excuse to throw whatever they want into it, without any organization or standardization... and the consequences of that are quite predictable. But it doesn't have to be that way. You can accumulate diverse and large data sets, in cheap cloud storage, while knowing what everything is and where you can find it. As a trivial example, let's say you have a typical OLTP database (or perhaps many), with useful data that is, unfortunately, mutable. You can store entire copies of those tables in your data lake for pennies a day, giving you the ability to recall a transactionally-consistent view of that data from various past times. This is something we've always been able to do using traditional tools, the difference is that storing the data in a "data lake" (i.e. S3) is orders-of-magnitude cheaper. Another major use case, perhaps the most significant one, is storing the raw ingested data -- e.g. from telemetry collection, 3rd party exports, etc -- along with each stage of its transformation. By preserving the original input, along with all intermediate outputs, no information is ever lost. If a buggy transformation is discovered it no longer means your output is irrevocably corrupted, fixed results can be re-computed from wherever in the transform pipeline the bug manifested. And again, this was always possible, a data lake just makes it cheap enough to actually do on a large scale.