Azure Data Engineering-Master 6 Real-World Projects

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“Learn to tackle real-world data engineering challenges with Azure by building hands-on projects in this comprehensive course. Dive into Azure’s data engineering services such as Data Factory, Azure SQL, Azure Storage Account, and Data Lake Storage to design, implement, and manage data pipelines. This course is tailored for data engineers, data scientists, and developers looking to enhance their skills and apply them in real-world scenarios.

No previous experience with Azure is required, but some background in data engineering and a general understanding of Azure will be beneficial. The course includes five practical projects that cover a range of use cases and scenarios for data engineering in Azure. By the end of this course, you will have the ability to design, construct, and manage data pipelines using Azure services.

This course, Azure for Data Engineering: Real-world Projects, focuses on five practical projects that address everyday data engineering issues using Azure technologies. With an emphasis on real-world scenarios, this course aims to provide you with the skills and knowledge to apply Azure to your own data engineering projects. Whether you are new to Azure or have some experience, this course is designed to help you take your data engineering skills to the next level.”

Is Azure good for data engineers?

Azure is a great choice for data engineers because it offers a comprehensive set of tools and services that make it easy to design, implement, and manage data pipelines. The Azure Data Factory, Azure SQL, Azure Storage Account, and Data Lake Storage are just a few of the services available to data engineers, making it easy to work with data no matter where it is stored.

One of the biggest advantages of using Azure for data engineering is the ability to easily integrate with other Azure services such as Azure Databricks, Azure Cosmos DB, and Power BI. This allows data engineers to build end-to-end solutions for data processing and analytics. Additionally, Azure provides options for data governance and security, which is a critical concern for data engineers.

In addition, Azure offers advanced features such as Azure Machine Learning and Azure Stream Analytics that can be used to optimize and scale data pipelines, allowing data engineers to quickly and easily process and analyze large amounts of data.

Overall, Azure provides a powerful and flexible platform for data engineers to work with, making it a great option for data engineering projects and real-world scenarios.

Project One: Simplifying Data Processing in Azure Cloud with Data Factory, Functions, and SQL

This course is designed for professionals and data enthusiasts who want to learn how to effectively use Azure cloud services to simplify data processing. The course covers the use of Azure Data Factory, Azure Functions, and Azure SQL to create a powerful and efficient data pipeline.

You will learn how to use Azure Data Factory to extract data from various online storage systems and then use Azure Functions to validate the data. Once the data is validated, you will learn how to use Azure SQL to store and process the data. Along the way, you will also learn best practices and case studies to help you build your own real-world projects.

This project is designed for professionals who want to learn how to use Azure Data Factory for efficient data processing in the cloud. The project covers the use of Azure functions and Azure SQL database for validation of source schema in Azure Data Factory.

The course starts with an introduction to Azure Data Factory and its features.

You will learn how to create and configure an Azure Data Factory pipeline and how to use Azure functions to validate source schema.

You will also learn how to use the Azure SQL database to store and retrieve the schema validation details.

Throughout the project, you will work on hands-on exercises and real-world scenarios to gain hands-on experience in implementing Azure Data Factory for data processing. You will learn how to use Azure functions to validate the source schema and how to use the Azure SQL database to store and retrieve the schema validation details.

By the end of this course, you will have a solid understanding of Azure Data Factory and its capabilities, and you will be able to use it to validate source schema using Azure functions and Azure SQL database. This will enable you to design and implement efficient data processing solutions in the cloud using Azure Data Factory, Azure functions, and Azure SQL database.”

This project is suitable for anyone with a basic understanding of data processing, who wants to learn how to use Azure cloud services to simplify data processing.

Project Two: Create dynamic mapping data flow in Azure data factory

In this project, you will learn how to use the powerful data flow feature in Azure Data Factory to create dynamic, flexible data pipelines. We will start by learning the basics of mapping data flows and how they differ from traditional data flows. From there, we will delve into the various components that make up a mapping data flow, including source, transformations, and sink. We will then explore how to use expressions and variables to create dynamic mappings and how to troubleshoot common issues. By the end of this course, you will have the knowledge and skills to create dynamic mapping data flows in Azure Data Factory to meet the specific needs of your organization. This course is ideal for data engineers and developers who are new to Azure Data Factory and want to learn how to build dynamic data pipelines.”

The project will cover the following topics:

1. Introduction to dynamic mapping data flow and its benefits
2. Understanding the concepts of mapping data flow and how it differs from traditional data flow
3. Hands-on exercises to create and configure dynamic mapping data flow in Azure Data Factory
4. Best practices for designing and implementing dynamic mapping data flow
5. Case studies and real-world examples of dynamic mapping data flow in action
6. Techniques for troubleshooting and optimizing dynamic mapping data flow
7. How to process multiple files with different schema.

These projects cover how you could reuse your mapping data flow, to process multiple files with different schema. It is very easy to design your mapping data flow and process files with the same schema. In this course, we will learn how you could create dynamic mapping data flow so that you could reuse your entire complicated transformations to transform your files and tables with different schema.

Project three: Real-time Project using Metadata Driven Framework in Azure Data Factory

Implement a Metadata driven framework to load multiple source tables from your source system to your Azure Storage account. In this project, we will take our azure data processing approach one step further by making ADF data pipelines metadata-driven. In a metadata-driven approach, you can process multiple tables and apply different transformations and processing tasks without redesigning your entire data flows.

This Project is designed to provide hands-on experience to the participants in implementing a real-time project using a metadata-driven framework in Azure Data Factory. The course will cover the concepts of a metadata-driven framework and its implementation in ADF. after this project, you will learn how to design and implement a metadata-driven ETL pipeline using ADF and how to use ADF’s built-in features to optimize and troubleshoot the pipeline.

By the end of the project, you will have a strong understanding of the Metadata Driven Framework in Azure Data Factory and how to use it in real-time projects. You will be able to design and implement data pipelines using the framework and will have the skills to optimize and troubleshoot them.

This project is perfect for data engineers, data architects, and anyone interested in learning more about the Metadata Driven Framework in Azure Data Factory.

Project Outline:

1. Introduction to Metadata Driven Framework in ADF
2. Setting up the Metadata Repository
3. Designing the Metadata-Driven Pipeline
4. Implementing the Metadata-Driven Pipeline
5. Optimizing and Troubleshooting the Pipeline
6. Real-time Project Implementation using Metadata Driven Framework
7. Case Studies and Best Practices

Prerequisites:

• Basic knowledge of Azure Data Factory
• Basic understanding of ETL concepts
• Familiarity with SQL scripting.

Target Audience:

• Data Engineers
• ETL Developers
• Data Architects

Project four: Incremental Data Loading in the Cloud: A Hands-on Approach with Azure Data Factory and Watermarking

In this project, you will learn how to implement incremental load using Azure Data Factory and a watermark table. This is a powerful technique that allows you to only load new or updated data into your destination, rather than loading the entire dataset every time. This can save a significant amount of time and resources.

You will learn how to set up a watermark table to track the last time a load was run and how to use this information in your ADF pipeline to filter out only new or updated data. You will also learn about the different types of incremental loads and when to use them. Additionally, you will learn about the benefits and best practices for using this technique in real-world scenarios. By the end of this course, you will have the knowledge and skills to implement incremental load in your own projects

This course will guide you through the process of how to efficiently load and process large amounts of data in a cost-effective and timely manner, while maintaining data integrity and consistency. The course will cover the theory and best practices of incremental loading, as well as provide hands-on experience through practical exercises and real-world scenarios. By the end of the course, you will have a solid understanding of how to implement incremental loading for multiple tables using Azure Data Factory and watermarking, and be able to apply this knowledge to your own projects

Project Five: Auditing and Logging Data Pipelines in Azure: A Hands-on Approach

In this project, you will learn how to implement a robust auditing and logging system for your Azure Data Factory pipelines using Azure SQL and stored procedures. You will gain a deep understanding of how to capture and store pipeline execution details, including start and end times, status, and error messages.

You will also learn how to use stored procedures to query and analyze your pipeline logs to identify patterns and trends. Throughout the project, you will work on real-world examples and use cases to solidify your knowledge and skills. By the end of this project, you will have the knowledge and skills needed to implement an efficient and effective auditing and logging system for your Azure Data Factory pipelines.

In this project, we will learn how to log audit details.

1. Using system variables.
2. Using the output of exciting activities.
3. Using the current item from your for each loop.
4. Using dynamic expressions.

By the end of the project, participants will have a thorough understanding of how to implement an advanced monitoring and auditing system for their Azure Data Factory pipelines and be able to analyze and troubleshoot pipeline performance issues more effectively.”

Project Six: Introductions To Azure Synapse Analytics And Azure Data Engineering

We are excited to announce the release of a new module in our Azure Data Engineering course, dedicated to explaining Azure Synapse Analytics. This module covers everything you need to know about Azure Synapse Analytics, from what it is and how to create it, to the different components and how to access data from an Azure Data Lake Gen2.

If you are not familiar with Azure Synapse Analytics, it is a limitless analytics service that brings together big data and data warehousing. It provides a unified experience for data ingestion, big data processing, and data warehousing, and allows you to query both structured and unstructured data using the same familiar tools and languages.

In our new module, we cover all the essential topics related to Azure Synapse Analytics, including:

• What is Azure Synapse Analytics, and why should you use it?
• How to create an Azure Synapse Analytics workspace
• The different components of Azure Synapse Analytics, such as SQL pools, Spark pools, and Pipelines
• How to access data from an Azure Data Lake Gen2 using Azure Synapse Analytics

We have designed this module to be easy to follow, with step-by-step instructions and real-world examples to help you understand how Azure Synapse Analytics works and how it can be used in your own projects.

By the end of this module, you will have gained a deep understanding of Azure Synapse Analytics and how it can be used to solve big data and data warehousing challenges. You will also have the skills necessary to create an Azure Synapse Analytics workspace and access data from an Azure Data Lake Gen2.

So, whether you are a data engineer, data scientist, or data analyst, our new module on Azure Synapse Analytics is the perfect way to deepen your knowledge of this powerful analytics service. Enroll in our Azure Data Engineering course today to access this new module and start learning!

Please Note: This course covers advanced topics in Azure Data Factory, and while prior knowledge of the platform is beneficial, it is not required as we will be covering all necessary details from the ground up. So, whether you’re new to Azure Data Factory or looking to expand your existing knowledge, this course has something to offer everyone

Please Note: This course comes with a 30-day money-back guarantee. If you are not satisfied with the course within 30 days of purchase, Udemy will refund your money, (Note: Udemy refund conditions are applied)