1. Obtain transparency about existing data
The creation of ETLs turned out to be a laborious and challenging process. It was often unclear where the data needed for engineering department queries was located and whether some of this data already existed in the data warehouse.
The first step required a suitable data catalog. With a suitable data catalog that had appropriate functionality, the engineering teams were able to get a complete and searchable overview of all the data. This overview also included the structures in the source systems, tools used and in the DWH itself.
Automation is essential to creating sustainable transparency. Keeping "up-to-date" should only mean as little effort as possible. The modern tools used were easily linked and synchronized.
2. Collaborative dataset creation
With individually selected and linked tools, any data from all sources can now be queried, combined and visualized.
Source structures can now be analyzed together with specialist colleagues, dynamically and in real time.
In a joint process, structures for helpful documentation describing all the necessary steps up to the final ETL were developed. The business glossary of the data catalog used promotes a common understanding of the data across teams.
3. Automated ETL synchronization
A data catalog is only as good as its content. That's why we helped customers establish an open and flexible data platform. In doing so, various APIs for data synchronization are supported. So regardless of which tools are used right now or in the future, there is always the possibility to keep all ETLs in sync and make them accessible across the entire team.
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