I'll now take you through my solution to the previous challenge. Even if you have already solved the task, I strongly recommend watching the video. As a result, you will either learn an alternative approach to solving this task or identify and adjust any mistakes in your own approach.

Here are the scripts used in my solution:

Master the essentials of data engineering with Microsoft Azure in this comprehensive course. Starting with foundational concepts like cloud computing, resource management, and storage solutions, you'll progress to hands-on training in Azure Data Factory (ADF), mastering ETL/ELT workflows, and advanced data transformations. Through practical examples and real-world problem-solving, you'll gain the skills to design, implement, and optimize scalable data solutions using Azure.

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.

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. 

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.

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.

Solving Loading Data Challenge

Solving Loading Data Challenge