DB-203
Introduction to Data Warehousing and OLAP
How We Conduct Training :
O&B Headquarters (3F Carlos J. Valdes Building, 108 Aguirre, Legazpi Village, Makati City)
Client's premises (Private sessions available with a minimum number of participants)
Course Overview
This course provides a foundational understanding of Data Warehousing (DW) and Online Analytical Processing (OLAP) principles. Participants will learn how to design and implement analytical databases that support complex business reporting and decision-making. The curriculum bridges the gap between traditional relational databases and high-performance analytical systems.
Throughout the training, students will explore the architecture of modern data stacks and the importance of dimensional modeling. We focus on the practical application of these concepts, ensuring that learners can transform raw data into actionable insights using standard SQL practices suitable for any major database platform.
Emphasis is placed on using standard SQL and universal database concepts for hosting data warehouse schemas. Students will learn to implement specialized structures like Star and Snowflake schemas to optimize query performance in a database-agnostic manner.
Prerequisite Courses
Learning Outcomes
- Define the core architecture and components of a Data Warehouse.
- Differentiate between OLTP (Transactional) and OLAP (Analytical) workloads.
- Design robust dimensional models consisting of Fact and Dimension tables.
- Implement Star and Snowflake schemas within a Relational Database Management System (RDBMS).
- Explain the stages of the ETL (Extract, Transform, Load) process.
- Perform multidimensional data analysis using slicing, dicing, and drill-down techniques.
Suitable For
Data Analysts, BI Developers, and SQL Professionals
Course Outline
Data Warehousing Foundations
- Evolution of Data Warehousing
- OLTP vs. OLAP Paradigms
- Dimensional Modeling (Facts & Dimensions)
- Star vs. Snowflake Schema Design
- Implementation in RDBMS (SQL)
- Slowly Changing Dimensions (SCD)
- ETL Process Overview
- OLAP Operations and Cube Concepts
Machine Problem & Code Review
- Design a Star Schema based on a real-world retail business scenario.
- Create and populate dimension tables using standard SQL.
- Develop a data transformation script to handle incremental data loads.
- Generate complex analytical reports using window functions and aggregations.
- Conduct a peer code review focusing on schema normalization and database-agnostic design patterns.
- Optimize query execution plans for large-scale analytical joins.
Software and Hardware Requirements
Hardware Requirements
- Minimum 8GB RAM
- Core i5 Processor (or equivalent)
- 20GB Available Disk Space
- Stable Internet Connection
Software Requirements
- Any mature relational database (e.g., PostgreSQL, MySQL, SQL Server, Oracle)
- SQL Client (e.g., DBeaver, pgAdmin, SSMS, SQL Developer)
- Schema Modeling Tool (e.g., Draw.io)
- Modern Web Browser