1. Prepare the migration ideally
Preparation is the crucial step for the success of a data warehouse migration. A modern data catalog is an important support for this. This enables a holistic overview of one's own data assets such as tables, views, ETLs, reports and more. This makes it clear how extensive the migration to a cloud DWH is.
Subsequently, existing dependencies are identified and documented, if not already done. Based on this, a prioritized migration plan is then drawn up.
A relevant issue for many customers in the context of a migration is also the topic of data quality. During the preparation phase, existing data and tables are reviewed to archive unused data and improve data quality. This step should also be supported by a data catalog and open source tools such as Great Expectations.Translated with
2. Actively shaping the migration
Throughout the process, the teams involved can get a complete overview of already migrated assets via the data catalog. Data lifecycles are redefined and automated checks are performed. This ensures high quality and reduces necessary repairs and subsequent maintenance.
Gaps between different DWH environments often occur. With suitable tools, they can bridge these efficiently. For example, existing documentation should be preserved and new components enriched with meaningful context.
Another helpful technology during the transition period is data virtualization. This allows you to easily perform cross-application queries, and combine data from multiple DWHs and other sources for ad-hoc queries.
3. Optimization of the data warehouse
Cloud DWHs support the long-term and smooth scalability of the data infrastructure. Collaborative work in diverse teams of experts is enormously simplified by providing information on dependencies, SQL statements, schedules and related assets.
Its full benefits unfold even more in combination with other tools in a modern data landscape. This allows data teams to quickly identify and make available source and target data structures. Business teams can, for example, independently create ad-hoc reports on prepared data sets at any time.
The combination with a data catalog, BI tools and ETL tools provides a clear overview. Available data structures and other assets can be permanently reused - across different teams. Modern data catalogs offer the advantage here of providing up-to-date information automatically and in real time. In addition, a correct and common understanding of the data is ensured by linking the technical and logical data model via the glossary.
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