Data lake, a term originally coined by James Dixon, the founder and CTO of Pentaho, is used to describe a data store which can scale to extremely large sizes, in an affordable manner. A data lake is also designed to store the raw data, in its original format, so it can be used immediately, rather than waiting weeks for the IT department to massage it into a format that the data warehouse can accept and/or use effectively.
The data lake concept always includes the capability to scale to an enormous size. However, you do not need petabytes of data to find use in a data lake. It can be used as cheap storage for long-term archival data. It can be used to transform data before attempting to ingest into a data warehouse with the convenience of retaining the original and transformed versions of the data. It also can be used as the centralized staging location for ingestion into the data warehouse, simplifying the loading processes.
I would like to take this opportunity to remind readers that the Aristotelian opposite of the Data Lake is the Data Swamp. Derik uses this term as well and it makes me feel warm and fuzzy inside to see broad adoption of this term.