Requirements

• You should have an interest in the fundamentals of Azure Data Engineering.
• Basic understanding of programming and algorithms

Description

Description

Take the next step in your career! Whether you're an aspiring data engineer, an experienced IT professional, a cloud solutions architect, or a data analyst, this course is your opportunity to sharpen your Azure Data Engineering skills, enhance your ability to design scalable data solutions, and advance your professional growth in the field of cloud-based data engineering.

With this course as your guide, you learn how to:

• Master the fundamental skills and concepts required for Azure Data Engineering, including SQL, Data Warehousing, ETL/ELT processes, and cloud-based data integration.

• Build and optimize data pipelines using Azure Data Factory (ADF), Databricks, Snowflake, PySpark, and Delta Tables, ensuring efficient data processing and transformation.

• Access industry-standard templates and best practices for data architecture, schema design, and performance optimization in cloud environments.

• Explore real-world applications of Azure services, including data lake storage, real-time analytics, data monitoring, and security best practices for enterprise-level data management.

• Invest in learning Azure Data Engineering today and gain the skills to design and manage scalable, high-performance data solutions that drive business success.

The Frameworks of the Course

Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises—this course is designed to explore Azure Data Engineering, covering SQL, Data Warehousing, ETL/ELT processes, and cloud-based data solutions using Azure services.

The course includes multiple case studies, resources such as templates, worksheets, reading materials, quizzes, self-assessments, and hands-on labs to deepen your understanding of Azure Data Engineering concepts and real-world applications.

• In the first part of the course, you’ll learn SQL basics and advanced techniques, data warehousing fundamentals, and data ingestion and transformation using Azure Data Factory (ADF) and Synapse Analytics.

• In the middle part of the course, you’ll develop a deep understanding of Databricks and PySpark, Delta Tables, versioning, and real-time data streaming using Azure Event Hub and Stream Analytics.

• In the final part of the course, you’ll gain expertise in Snowflake for Data Engineering, designing production pipelines, CI/CD implementation with Azure DevOps, and monitoring data workflows.

  
  

Part 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. SQL Basics and Advanced Concepts

1.1. Introduction to SQL

1.1.1. Basics of relational databases and SQL.

1.1.2. SQL syntax and query structure.

1.1.3. SELECT, WHERE, GROUP BY, and ORDER BY clauses

1.2. Advanced SQL techniques

1.2.1. Joins (INNER, OUTER, LEFT, RIGHT).

1.2.2. Subqueries, CTEs, and Window Functions.

1.2.3. Aggregations and analytical functions.

1.3. SQL for Data Engineering

1.3.1. Data manipulation and transformation.

1.3.2. Handling large datasets and performance tuning.

1.3.3. Data ingestion and validation using SQL.

Module 2. Data Warehousing Concepts

2.1. Introduction to Data Warehousing

2.1.1. OLTP vs. OLAP.

2.1.2. Star and Snowflake schema designs.

2.1.3. Dimensional modeling concepts.

2.2. Data Pipeline Design

2.2.1. ETL vs. ELT processes.

2.2.2. Data staging, integration, and transformation layers.

2.3. Hands-On Activity

2.3.1. Creating sample schemas and loading sample data.

Module 3. Azure Data Engineering Fundamentals

3.1. Overview of Azure Data Engineering

3.1.1. Introduction to Azure cloud platform.

3.1.2. Key Azure services for Data Engineering.

3.2. Azure Storage Solutions

3.2.1. Azure Data Lake Storage.

3.2.2. Blob storage and file management.

3.2.3. Security and access control mechanisms.

3.3. Azure Data Integration

3.3.1. Introduction to Azure Synapse Analytics.

3.3.2. Data movement and integration tools in Azure.

Module 4. Azure Services for Data Engineering

4.1. Azure Functions and Logic Apps

4.1.1. Automating workflows using Logic Apps.

4.1.2. Serverless computing with Azure Functions.

4.2. Azure Event Hub and Stream Analytics

4.2.1. Streaming data ingestion.

4.2.2. Real-time analytics in Azure.

4.3. Monitoring and Optimization

4.3.1. Cost optimization techniques.

4.3.2. Monitoring and debugging Azure workloads

Module 5. Azure Data Factory (ADF)

5.1. Introduction to Azure Data Factory

5.1.1. ADF architecture and components.

5.1.2. Pipelines, triggers, and datasets.

5.2. Building ETL Pipelines in ADF

5.2.1. Creating and managing data pipelines.

5.2.2. Data transformations using ADF.

5.3. Integration with Other Services

5.3.1. Integrating ADF with Databricks, SQL server, and Snowflake.

5.4. Hands-On Activity

5.4.1. Building a sample ETL pipeline in ADF.

Module 6. Databricks and PySpark

6.1. Introduction to Databricks

6.1.1. Overview of Databricks and its architecture.

6.1.2. Setting up Databricks workspaces.

6.2. Introduction to PySpark

6.2.1. Basics of distributed computing.

6.2.2. Dataframes, RDDs, and Spark SQL.

6.3. Advanced PySpark Techniques

6.3.1. Writing and optimizing PySpark jobs.

6.3.2. Working with large datasets.

6.4. Hands-On Activities

6.4.1. Building PySpark applications.

6.4.2. Integrating Databricks with Azure services.

Module 7. Delta Tables and Versioning

7.1. Delta Lake Fundamentals

7.1.1. Overview of Delta tables.

7.1.2. ACID transactions and schema enforcement.

7.2. Versioning and Time Travel

7.2.1. Querying data at specific points in time.

7.2.2. Implementing CDC (Change Data Capture) workflows.

Module 8. Snowflake Core Concepts

8.1. Introduction to Snowflake

8.1.1. Architecture and key features of Snowflake.

8.1.2. Warehouses, databases, and schema in Snowflake.

8.2. Data Loading and Querying in Snowflake

8.2.1. Copying data into Snowflake.

8.2.2. Writing and optimizing queries.

8.3. Snowflake for Data Engineering

8.3.1. Integration with Azure services.

8.3.2. Best practices for using Snowflake in production.

Module 9. Production Pipelines and Deployment

9.1. Designing Production Pipelines

9.1.1. Best practices for scalable pipelines.

9.1.2. Handling exceptions and retries.

9.2. CI/CD for Azure Data Engineering

9.2.1. Using Azure DevOps for pipeline deployment.

9.2.2. Version control and automated testing.

9.3. Monitoring and Maintenance

9.3.1. Monitoring data pipelines in production.

9.3.2. Troubleshooting and performance tuning.

Part 2

Module 10. Capstone Project

10.1. Project Design and Implementation

10.1.1. Design a complete Data Engineering solution.

10.1.2. Use Azure services, Databricks, Snowflake, and PySpark.

Who this course is for:

• Data professionals looking to gain expertise in SQL
• Data Warehousing
• and ETL/ELT processes for efficient data management and transformation.
• New professionals seeking to build a career in Azure Data Engineering by learning cloud-based data solutions
• data pipeline development
• and big data processing using Azure services.
• Existing data engineers
• architects
• and IT professionals who want to enhance their skills in their respective domain to optimize data workflows and improve performance.
• Technical leads
• managers
• and decision-makers looking to understand scalable data engineering architectures
• cloud-based data integration strategies
• and real-time data analytics using Azure.