Introduction

Data warehousing and ETL (Extract, Transform, Load) processes are fundamental components of modern data engineering. A well-designed data warehouse enables businesses to consolidate, store, and analyze large volumes of historical and operational data, facilitating decision-making and business intelligence. The ETL process ensures that this data is properly extracted from various sources, transformed to meet analytical needs, and loaded into the warehouse.

This course focuses on teaching data engineers how to design efficient data warehousing systems and robust ETL pipelines. You will explore key concepts of dimensional modeling, data warehouse architecture, and best practices for designing scalable and maintainable ETL pipelines. Additionally, you will work with popular tools and technologies used for data warehousing and ETL processes, including cloud-based solutions like Amazon Redshift, Google BigQuery, and Azure Synapse Analytics.

By the end of this course, you will have the skills to design data warehousing solutions and ETL workflows that are optimized for performance, scalability, and data integrity.

Prerequisites

• Basic knowledge of database management systems (DBMS).
• Familiarity with SQL and data modeling concepts.
• Understanding of basic data engineering concepts (e.g., data pipelines, data integration).
• Experience with cloud platforms (optional but beneficial).

Table of Contents

1. Introduction to Data Warehousing
   1.1 What is Data Warehousing?
   1.2 Benefits and Challenges of Data Warehousing
   1.3 Key Components of a Data Warehouse: Staging, Data Marts, and OLAP
   1.4 Data Warehouse Architectures: Traditional vs. Cloud-Based
2. Dimensional Modeling and Design
   2.1 Introduction to Dimensional Modeling
   2.2 Star Schema vs. Snowflake Schema
   2.3 Fact and Dimension Tables: Best Practices
   2.4 Designing Slowly Changing Dimensions (SCD)
   2.5 Normalization and Denormalization in Data Warehousing
3. ETL Fundamentals for Data Engineers
   3.1 Overview of the ETL Process
   3.2 Extracting Data from Multiple Sources: Databases, APIs, Files
   3.3 Data Transformation: Cleansing, Normalizing, and Aggregating
   3.4 Loading Data into Data Warehouses and Data Lakes
   3.5 ETL Best Practices for Performance and Scalability
4. ETL Tool Selection and Architecture
   4.1 Choosing the Right ETL Tool for Your Use Case
   4.2 Comparing Open-Source and Commercial ETL Tools
   4.3 Using Apache Nifi for Data Integration
   4.4 Working with Talend and Microsoft SSIS for ETL Automation
   4.5 Cloud-Based ETL Tools: AWS Glue, Azure Data Factory, Google Dataflow
5. Designing Scalable and Efficient ETL Pipelines
   5.1 Understanding Batch vs. Stream Processing in ETL
   5.2 Handling Data Volume and Velocity: Scalable ETL Architectures
   5.3 Optimizing ETL Performance with Parallel Processing and Partitioning
   5.4 Error Handling and Logging in ETL Pipelines
6. Data Integration and Aggregation
   6.1 Integrating Data from Multiple Sources: Relational, NoSQL, and Cloud
   6.2 Data Aggregation Techniques for Analytical Queries
   6.3 Handling Real-Time Data Integration with Apache Kafka and Spark
   6.4 Using Data Lakes for Raw and Semi-Structured Data Storage
7. Data Warehouse Automation and Orchestration
   7.1 Introduction to Data Warehouse Orchestration
   7.2 Scheduling ETL Jobs with Apache Airflow
   7.3 Automating Data Pipeline Workflows with Cloud Tools (AWS, GCP, Azure)
   7.4 Monitoring and Managing Data Warehouse Performance
8. Data Warehouse Security and Compliance
   8.1 Securing Data in Data Warehouses
   8.2 Access Control and Encryption Best Practices
   8.3 Ensuring Data Quality and Integrity in ETL Pipelines
   8.4 Compliance Requirements for Data Warehousing (GDPR, HIPAA, etc.)
9. Cloud-Based Data Warehousing
   9.1 Introduction to Cloud Data Warehousing (Amazon Redshift, BigQuery, Snowflake)
   9.2 Migrating Traditional Data Warehouses to the Cloud
   9.3 Managing Cloud Data Warehouses: Cost Optimization and Scaling
   9.4 Integrating Cloud-Based Data Warehouses with Data Lakes and ETL Pipelines
10. Real-World Use Cases and Best Practices
    10.1 End-to-End Data Warehousing and ETL Pipeline Example
    10.2 Building a Scalable Data Warehouse with AWS Redshift
    10.3 Optimizing ETL for Cloud and On-Premise Data Warehouses
    10.4 Best Practices for Maintaining and Scaling Data Warehouses and ETL Pipelines

Conclusion

By completing this course, you will gain a comprehensive understanding of how to design and implement data warehousing systems and ETL pipelines that are scalable, efficient, and maintainable. You will be equipped to tackle complex data engineering challenges, integrate disparate data sources, and design high-performance data warehouses optimized for both batch and real-time processing.

You will also be well-versed in best practices for data transformation, error handling, and security, ensuring that your data engineering solutions meet the highest standards of quality and compliance. Whether working on-premises or in the cloud, this course will provide you with the tools and techniques to build data infrastructure that supports business intelligence, analytics, and decision-making at scale.