Data lineage is defined as a type of data life cycle. It is a detailed representation of any data over time: its origin, processes, and transformations. Although this isn’t a brand new concept, a paradigm shift is taking place…
Obtaining data lineage from a Data Warehouse, for example, was a pretty simple task. This centralized storage system allowed, “by design,” you to obtain data lineage from the data stored in the same place.
The data ecosystem has been evolving at a very rapid pace since the emergence of Big Data due to the appearance of various technologies and storage systems that complicate information systems in enterprises.
It has become impossible both to keep and to impose a single centralized tool in organizations. Softwares and methods used by urbanists and IS architects of the “old world” have become less and less maintainable, making their work obsolete and illegible.
So, how can you visualize an efficient data lineage in a Big Data environment?
In order to have a global vision of an enterprise’s IS data, new tools are emerging. We are talking about a data catalog. It allows for a maximum amount of metadata from all data storages to be treated via a user-friendly interface. By centralizing all of this information, it is possible to create data lineage in a Big Data environment at different levels:
However, this data lineage standard on its own does not make it possible for data users to answer all of their questions. Among others, these questions remain: what about sensitive data? What columns were created and with what processes? etc.
