Introduction to Data Engineering with Azure

Introduction to Data Engineering with Azure

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1. Getting Started with Azure and Core Tools

Have a deep dive into the foundational concepts and tools required to begin your journey with Microsoft Azure. You'll explore cloud computing basics, learn how to organize and manage resources efficiently using Azure Resource Groups, and set up core services like Blob Storage and Azure SQL Database. Additionally, you’ll secure sensitive data with Azure Key Vault and gain an overview of Azure Data Factory (ADF) to understand its role in modern data engineering workflows.

Getting Started

Understanding Azure Subscriptions

Organizing Resources with Resource Groups

Using Azure Blob Storage for Data Storage

Setting Up Azure SQL Database

Managing Credentials with Azure Key Vault

Introduction to Azure Data Factory

2. Foundations of Azure Data Factory

Explore the core functionalities of Azure Data Factory (ADF), a powerful tool for creating and automating ETL/ELT workflows. In this section, you'll learn how to configure services, datasets, and pipelines while exploring key activities such as Copy, Lookup, and Script. You'll also work with advanced features like the ForEach activity for iteration and pipeline parameters for flexibility.

Linked Services

What is ADF Pipeline?

Script Activity

Lookup Activity

Dataset Parameters

ForEach Activity

Execute Pipeline Activity

3. Data Flows and Transformations in ADF

This section focuses on leveraging Azure Data Factory's data flow capabilities to transform and enrich your data. You'll learn to connect various data sources, create derived columns, and merge datasets using joins. Practical examples will guide you through filtering, sorting, conditional splits, and aggregating data to generate meaningful insights. By mastering these transformations, you'll be equipped to build robust, efficient ETL workflows tailored to real-world data engineering challenges.

Understanding Data Flows in Azure Data Factory

Derived Column Transformation

Combining Data with Joins

Using Conditional Split and Filtering in ADF

Grouping, Select, and Sort Transformations in ADF

4. Practical Problem Solving with ADF

Apply your knowledge to real-world data engineering scenarios in this hands-on section. You'll tackle practical challenges like loading and analyzing data for different categories, identifying patterns, and assessing financial health using advanced ADF workflows. By solving these problems, you'll gain experience in designing end-to-end data solutions, working with diverse datasets, and generating actionable insights.

Challenge: Loading Data to Several Tables

Solving Loading Data Challenge

Challenge: Conducting Transaction Analysis

Solving Conducting Analysis Challenge

Course Content

Introduction to Data Engineering with Azure

Introduction to Data Engineering with Azure

1. Getting Started with Azure and Core Tools

Getting Started Understanding Azure Subscriptions Organizing Resources with Resource Groups Using Azure Blob Storage for Data Storage Setting Up Azure SQL Database Managing Credentials with Azure Key Vault Introduction to Azure Data Factory

2. Foundations of Azure Data Factory

Linked Services Datasets What is ADF Pipeline?Script Activity Lookup Activity Dataset Parameters ForEach Activity Execute Pipeline Activity

3. Data Flows and Transformations in ADF

Understanding Data Flows in Azure Data Factory Derived Column Transformation Combining Data with Joins Using Conditional Split and Filtering in ADF Grouping, Select, and Sort Transformations in ADF

4. Practical Problem Solving with ADF

Challenge: Loading Data to Several Tables Solving Loading Data Challenge Challenge: Conducting Transaction Analysis Solving Conducting Analysis Challenge

Dataset Parameters

Parameters in Azure Data Factory are dynamic elements that make pipelines, datasets, and linked services flexible and reusable. By leveraging parameters, you can create solutions that adapt to different scenarios with minimal configuration changes. In this chapter, we'll focus on dataset parameters, particularly using them to define the table name dynamically for a SQL dataset.

For example, in a SQL dataset, instead of hardcoding the table name, you can use a parameter to set the table name at runtime. This approach is especially useful when working with multiple tables or environments.

We created a dataset with a table name parameter to enable dynamic table selection during data operations. By defining a parameter for the table name in a SQL dataset, you can reuse the same dataset for multiple tables, making your workflows more efficient and adaptable.

This setup lays the foundation for the ForEach Activity, where we will dynamically iterate over multiple table names and perform operations on them. This approach is essential for scalable and flexible data integration pipelines.

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Section 2. Chapter 6

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