Course Overview
This course covers the fundamentals of data warehousing and Extract, Transform, Load (ETL) processes. You’ll learn how to design, build, and manage data warehouses, integrate data from multiple sources, and optimize data pipelines for business intelligence and analytics. By the end of the course, you’ll have hands-on experience in implementing ETL workflows and managing large-scale data storage.
Course Content
Module 1: Introduction to Data Warehousing
- What is a data warehouse?
- Data warehouse vs. database
- Components & architecture of a data warehouse
Module 2: Data Modeling & Schema Design
- Star schema vs. Snowflake schema
- Fact & dimension tables
- Designing efficient data models for analytics
Module 3: Understanding ETL Processes
- Extract, Transform, Load (ETL) workflow
- ETL vs. ELT: Key differences
- Popular ETL tools (Talend, Apache NiFi, Airflow)
Module 4: Data Extraction Techniques
- Extracting data from structured & unstructured sources
- Working with APIs, databases, and cloud storage
- Automating data extraction workflows
Module 5: Data Transformation & Cleaning
- Handling missing & inconsistent data
- Data deduplication & normalization
- Using Python & SQL for data transformation
Module 6: Data Loading & Performance Optimization
- Loading data into data warehouses (Redshift, Snowflake, BigQuery)
- Batch vs. real-time data processing
- Optimizing ETL pipelines for efficiency
Module 7: Data Warehouse Implementation & Automation
- Setting up a cloud-based data warehouse
- Automating ETL workflows with Apache Airflow
- Monitoring & maintaining data pipelines
Module 8: Capstone Project & Certification
- Building a complete ETL pipeline
- Designing and deploying a scalable data warehouse
- Project submission & certification
Who Should Enroll?
- Data engineers & analysts working with large datasets
- Business intelligence professionals & database administrators
- Anyone interested in learning ETL processes for data management
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