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Learn how a modern data architecture allows you to use a data catalog, data fabric and data observability to build resilient DataOps pipelines to create a data mesh of reusable data products published in a data marketplace

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 about the definition and benefits of Data Products in accelerating the creation of data for use in multiple analytical workloads, delve into the key components for speeding up the development of Data Products, and uncover the initial steps to build your Data Product strategy.

Learn how to set up an AI-driven enterprise data governance program to systematically govern data and content across your distributed data estate.

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

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.

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

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

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 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?

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

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.

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