This course is made for data professionals who want to go beyond running SQL queries and start designing structured, scalable, and analytics-ready data warehouses. From building star schemas to writing ETL processes and optimizing performance for reporting, this hands-on course shows you how to structure your data the right way—so it’s fast, reliable, and ready for decision-making.
Day 1: Data Warehousing Concepts and SQL Design
Session 1: What Makes a Data Warehouse Different
OLAP vs OLTP, facts and dimensions explained
Star schema, snowflake schema, and normalization trade-offs
Session 2: Building Warehouse Tables in SQL
Creating dimension and fact tables
Constraints, keys, and data types for analytical workloads
Day 2: ETL Workflows and Data Loading
Session 1: Designing and Writing ETL Pipelines
Extracting from sources, transforming logic, and loading targets
Staging tables, error handling, and logging
Session 2: SQL for Analytics and Business Use
Window functions, aggregations, and derived metrics
Common reporting queries and use cases
Day 3: Optimization, Governance, and Use Cases
Session 1: Query Tuning and Index Optimization
Partitioning, indexing, and statistics for large datasets
Performance troubleshooting and best practices
Session 2: Final Use Case and Wrap-Up
Group project: model and populate a mini data warehouse
Hands-on insights, Q&A, and strategy for production rollout
We are open to customizing this program to align with your specific learning objectives. If your team has particular goals or areas they wish to focus on, we would be happy to tailor the course outline to meet those needs and ensure the program supports the achievement of your desired outcomes.
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This course is made for data professionals who want to go beyond running SQL queries and start designing structured, scalable, and analytics-ready data warehouses
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