  Data Lake (DL) is a Big Data analysis solution which ingests raw data in
their native format and allows users to process these data upon usage. Data
ingestion is not a simple copy and paste of data, it is a complicated and
important phase to ensure that ingested data are findable, accessible,
interoperable and reusable at all times. Our solution is threefold. Firstly, we
propose a metadata model that includes information about external data sources,
data ingestion processes, ingested data, dataset veracity and dataset security.
Secondly, we present the algorithms that ensure the ingestion phase (data
storage and metadata instanciation). Thirdly, we introduce a developed metadata
management system whereby users can easily consult different elements stored in
DL.
