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The word “Kimball” is synonymous with dimensional modeling. Learn how to apply Ralph’s techniques for developing your dimensional model, from the basics to the most advanced.

Learn how to assess your existing data architecture, define future requirements and design a new modern data architecture.

Learn about the inner workings and capabilities of data catalogs, along with mastering their utilization for data discovery, classification, and cataloging across multiple data stores both on premises, across multiple clouds, and at the edge.

Experience the full potential of Data Warehouse Automation technology by attending a 4-hour hands-on session.

Learn how to build a data strategy for a data-driven enterprise that delivers business value while improving data governance.

What is a Data Mesh and how does it differ from a Data Lake and a Data Lakehouse? What benefits does a Data Mesh offer and what are the implementation options? Learn about the principles, requirements, and challenges of implementing a Data Mesh.

Learn how you can embed BI, ML and AI-automation into applications and processes to make your company data and AI-driven.

Learn how to migrate a data warehouse schema, data and ETL jobs and security while minimizing the impact on business users. Should you migrate an existing data warehouse ‘as is’ or try to make changes during migration?

Learn the essential elements of the popular Kimball approach as described in the bestselling book, The Data Warehouse Lifecycle Toolkit.

Learn the tools and techniques needed to capture new data types, establish a data pipeline to produce re-usable data assets, modernize your data warehouse and bring together data and analytics.

Learn what is needed to discover, classify and govern data and content by using a data governance framework. And learn key technologies like data catalogs, data classifiers, data fabric and MDM.

This course helps you understand all the factors necessary for effectively designing the back room ETL system of your Kimball DW/BI environment.

Get you up to scratch on big data technologies such as Spark, Cloud Storage Data Lakes, Hadoop, Lakehouses, Flink, Analytical SQL, NoSQL DBMSs and Multi-Platform Analytics.