Data Warehousing and ETL with SQL Server & Azure Training (DW 107)

DW-107 is designed to provide a comprehensive understanding of data warehousing concepts, ETL processes, and modern data platform solutions using Microsoft technologies. Participants will learn how to design, build, and manage scalable data warehouses using SQL Server and Azure services.

This course covers core data warehousing concepts, including data modeling, ETL development, and analytics. Learners will gain practical experience with tools like SQL Server Integration Services (SSIS), Azure Data Factory, and Azure Synapse Analytics.

The course focuses on real-world implementation scenarios such as building data pipelines, transforming data, and optimizing performance.

Module 1: Introduction to Data Warehousing

Topics Covered:

• Data warehousing concepts
• OLTP vs OLAP
• Data warehouse architecture
• Star and Snowflake schemas

Lab:

• Analyze sample business data
• Design a basic data warehouse architecture
• Identify fact and dimension tables

Outcome:
Understand data warehouse fundamentals and architecture.

Module 2: Data Modeling Techniques

Topics Covered:

• Dimensional modeling
• Fact tables and dimension tables
• Slowly Changing Dimensions (SCD)

Lab:

• Design a star schema
• Implement dimension tables
• Apply SCD techniques

Outcome:
Create optimized data models for analytics.

Module 3: ETL Concepts and Tools

Topics Covered:

• ETL vs ELT
• Data extraction, transformation, loading
• Introduction to SSIS and Azure Data Factory

Lab:

• Build an ETL pipeline using SSIS
• Extract data from multiple sources
• Transform and load into warehouse

Outcome:
Develop and manage ETL processes.

Module 4: Azure Data Factory Pipelines

Topics Covered:

• Data Factory architecture
• Pipelines and activities
• Data movement and transformation

Lab:

• Create a pipeline in Azure Data Factory
• Schedule and automate workflows
• Monitor pipeline execution

Outcome:
Automate data workflows in Azure.

Module 5: Azure Synapse Analytics

Topics Covered:

• Synapse architecture
• Data warehousing in Azure
• Query performance optimization

Lab:

• Create a Synapse workspace
• Load data into a data warehouse
• Run analytical queries

Outcome:
Work with modern cloud-based data warehouse solutions.

Module 6: Data Integration and Transformation

Topics Covered:

• Data cleansing
• Data transformation techniques
• Handling large datasets

Lab:

• Clean and transform raw data
• Implement business rules
• Prepare data for reporting

Outcome:
Ensure high-quality, analysis-ready data.

Module 7: Data Security and Governance

Topics Covered:

• Data security best practices
• Role-based access control (RBAC)
• Data governance and compliance

Lab:

• Configure access control
• Secure data warehouse resources
• Audit data usage

Outcome:
Secure and govern data warehouse environments.

Module 8: Reporting and Visualization Integration

Topics Covered:

• Integration with Power BI
• Creating dashboards and reports
• Data visualization best practices

Lab:

• Connect Power BI to data warehouse
• Create dashboards and reports
• Share insights with stakeholders

Outcome:
Deliver business insights using reporting tools.