> It is important that all data put in the lake should have a clear provenance in place and time. Every data item should have a clear trace to what system it came from and when the data was produced. The data lake thus contains a historical record. This might come from feeding Domain Events into the lake, a natural fit with Event Sourced systems. But it could also come from systems doing a regular dump of current state into the lake - an approach that's valuable when the source system doesn't have any temporal capabilities but you want a temporal analysis of its data. A consequence of this is that data put into the lake is immutable, an observation once stated cannot be removed (although it may be refuted later), you should also expect ContradictoryObservations.

> The data lake is schemaless, it's up to the source systems to decide what schema to use and for consumers to work out how to deal with the resulting chaos. Furthermore the source systems are free to change their inflow data schemas at will, and again the consumers have to cope. Obviously we prefer such changes to be as minimally disruptive as possible, but scientists prefer messy data to losing data.

So basically, a data lake is a fancy word for a file system?