This course provides participants with the knowledge and practical skills to design, implement, and query data warehouses using SQL-based technologies.

By the end of the course, participants will be able to:

• Understand the concepts and architecture of data warehousing.
• Differentiate between OLTP and OLAP systems.
• Design star and snowflake schemas for analytical purposes.
• Implement ETL (Extract, Transform, Load) processes.
• Write efficient SQL queries for reporting and analytics.
• Apply optimization techniques for query performance.
• Explore integration with BI tools for visualization and decision-making.

• Database administrators and developers.
• Data analysts and business intelligence professionals.
• IT professionals involved in data management.
• Project managers overseeing data-driven initiatives.
• Anyone seeking to enhance their SQL and data warehousing skills.

• Interactive Lectures: Conceptual explanations with real-world examples.
• Case Studies: Industry scenarios to apply learned concepts.
• Group Discussions: Collaborative problem-solving and design exercises.
• Assessments: Daily quizzes and final project presentation.

Day 1 – Foundations of Data Warehousing

• Introduction to Data Warehousing concepts
• OLTP vs OLAP systems: differences and use cases
• Data Warehouse architecture: staging, integration, presentation layers
• Types of data warehouses (enterprise, data marts, operational)
• Key terminology: facts, dimensions, measures, granularity
• Hands-on: Exploring sample warehouse structures

Day 2 – Dimensional Modeling

• Principles of dimensional modeling
• Star schema vs snowflake schema design
• Fact tables: transaction, snapshot, accumulating facts
• Dimension tables: slowly changing dimensions (SCD types 1, 2, 3)
• Hierarchies and drill-down analysis
• Best practices for schema design
• Hands-on: Designing a star schema for a retail case study

Day 3 – ETL Processes

• ETL overview: Extract, Transform, Load
• Data extraction from heterogeneous sources
• Data cleaning, validation, and transformation techniques
• Loading strategies: full load vs incremental load
• ETL tools overview (SSIS, Informatica, Talend)
• Error handling and logging in ETL pipelines
• Hands-on: Building a simple ETL workflow with SQL scripts

Day 4 – SQL for Data Warehousing

• Advanced SQL concepts for analytics
• Joins, aggregations, and grouping sets
• Window functions (RANK, ROW_NUMBER, LAG/LEAD)
• Subqueries and common table expressions (CTEs)
• Materialized views and summary tables
• Query optimization basics: indexing, partitioning
• Hands-on: Writing analytical queries on warehouse tables

Day 5 – Performance, BI Integration & Capstone

• Query performance tuning and optimization techniques
• Indexing strategies and partitioning for large datasets
• Data warehouse security and access control
• Integration with BI tools (Power BI, Tableau, Qlik)
• Building dashboards and reports from warehouse data
• Capstone project:
• Design a mini data warehouse schema
• Implement ETL for sample data
• Write SQL queries for reporting
• Present findings and dashboards

RECTUS attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.