How to get the most out of your cloud data warehouse!

We help our customers get the most out of their data warehouse (DWH). With our best practices, your cloud migration will be successful and you will get the most out of your DWH!

In 3 steps to your own DWH cloud instance

1. Preparation
2. Transition
3. Optimization

1. Prepare the migration ideally

Preparation is the decisive step for the success of a DWH migration. An important tool here is a modern data catalog. It supports three central aspects of preparation and planning.

Overview of your data landscape

A data catalog enables you to get a holistic overview of your own data assets such as tables, views, ETLs, reports and more. A correct overview is the basis for estimating how extensive the migration to a cloud DWH will be.

Understand relations between data assets

Additionally, we recommend identifying existing dependencies and documenting them where not already done. With the knowledge thus gained and the transparency about the data landscape, you can then draw up a prioritized migration plan.

Sustainably improve data quality

A relevant issue for many customers in the context of a migration is the topic of data quality. The preparation phase can be optimally used to

  • check already existing data and tables

  • archive unused data

  • improve the data quality.

In addition to a data catalog, we recommend the use of open source tools such as Great Expectations.

Overview on data assets in a data catalog, here linkedin datahub
Get an overview of data
detailed information about status of a dataset in a data catalog, here linkedin datahub
Enable monitoring

2. Actively shape the migration

Even during the actual migration, certain activities have emerged as success factors in numerous projects.

Monitoring the migration progress

A data catalog is also a great supporter of the process during migration! It allows the teams involved to get a complete overview of already migrated assets at any time.

Establish control mechanisms

Migration is a good time to establish automated checks and redefine data lifecycles - because this ensures high data quality and reduces necessary repairs and subsequent maintenance. Modern tools offer very convenient, powerful functions for this purpose.

Modern tools for bridging

Gaps between different DWH environments often occur. With suitable tools, you can bridge these efficiently. 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.

Improve the documentation

Preserve existing documentation - a data catalog is also suitable for this purpose.  It is best to enrich new components with meaningful context directly during migration.

3. Optimize your cloud DWH usage

Cloud DWHs support the long-term and smooth scalability of the data infrastructure. Collaborative work in different teams can also be simplified enormously. Cloud DWHs unfold their advantages best in combination with other tools in a modern data landscape.

Bringing together different tools allows business teams, for example, to independently create ad hoc reports on prepared data sets at any time. It also allows available data structures and other assets to be permanently reused - across different teams. This saves data engineers a lot of work. Our advice is: bring your data catalog, your BI tools and your ETL tools together for maximum added value.

Another benefit of linking the DWH with many other tools is that collaborative data engineering is greatly simplified. Different teams of experts can share information about dependencies, SQL statements, schedules, and related assets from different applications.

overview on data lineage for migration organization, here in linkedin datahub
Visualize dependencies
Let's talk together about your challenges.

We are always there for our customers. Simply contact us for a non-binding conversation.

Mike Linthe
Kira Lenz
Michael Franzkowiak
Ian Solliec
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