Star Schemas to Snowflake: Data Modeling for Analytics Teams
About this course
Data warehouses fail not because of bad data — but because of bad design. Poorly structured schemas slow queries, inflate costs, and force analysts to rely on IT for every report. This program teaches you how to prevent that from the ground up. Star Schemas to Snowflakes is an advanced-level program designed for data engineers, analytics engineers, database administrators, and platform architects who are ready to build data infrastructure that performs at enterprise scale. Across nine focused courses, you will master dimensional modeling using star and snowflake schemas, normalize and optimize relational databases for query performance, implement Slowly Changing Dimensions, automate checksum validation, provision cloud data warehouses using Infrastructure as Code, architect disaster recovery systems, and manage capacity and cost across multi-cluster environments. You will work with industry tools and frameworks including SQL, Terraform, PostgreSQL, and Tableau, applying skills in realistic scenarios drawn from production data environments. Every course combines concise instruction with hands-on projects that produce real, applicable artifacts. By the end of the program, you will be equipped to design, deploy, scale, and govern analytics data infrastructure — with the technical depth and business judgment that modern data teams require.
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What you'll learn
- master dimensional modeling using star and snowflake schemas
- normalize and optimize relational databases for query performance
- implement Slowly Changing Dimensions
- automate checksum validation
- provision cloud data warehouses using Infrastructure as Code
- architect disaster recovery systems
- manage capacity and cost across multi-cluster environments
Course objectives
- equip learners to design, deploy, scale, and govern analytics data infrastructure
- provide hands-on experience with industry tools such as SQL, Terraform, PostgreSQL, and Tableau
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