Azure Data Engineering-Master 8 Real-World Projects

Master Azure Data Engineering: Build Real-World Data Pipelines with Hands-On Projects

Unlock Your Data Engineering Potential with Azure

Are you a data engineer, data scientist, or developer looking to elevate your skills and tackle real-world data challenges? This comprehensive course provides a practical pathway to mastering Azure's powerful data engineering services. Learn to design, implement, and manage robust data pipelines using industry-leading tools like Azure Data Factory, Azure SQL, Azure Storage Account, and Data Lake Storage.

Why Choose Azure for Data Engineering?

Azure offers a comprehensive suite of tools designed for efficient data management and processing. Key advantages include:

• Scalability & Flexibility: Easily scale your data pipelines to handle large volumes of data.
• Seamless Integration: Integrate effortlessly with other Azure services like Azure Databricks, Azure Cosmos DB, and Power BI for end-to-end data solutions.
• Data Governance & Security: Robust features to ensure data security and compliance.
• Advanced Analytics: Leverage Azure Machine Learning and Azure Stream Analytics for data optimization and real-time processing.

No prior Azure experience is required, but a foundational understanding of data engineering principles is beneficial. This course features five practical projects designed to address common data engineering issues and equip you with job-ready skills.

Course Highlights:

Project 1: Simplifying Data Processing in Azure Cloud with Data Factory, Functions, and SQL

Learn to streamline data processing using Azure Data Factory, Azure Functions, and Azure SQL. Extract data from various sources, validate it with Azure Functions, and store/process it in Azure SQL. This project covers:

• Azure Data Factory fundamentals and pipeline creation.
• Using Azure Functions for data validation.
• Storing and retrieving schema validation details in Azure SQL.
• Best practices for efficient data processing.

Project 2: Real-Time Data Pipeline Project with Azure

Build a complete, scalable, cloud-native data engineering solution. This project focuses on:

• Extracting data from public and enterprise APIs.
• Automating ingestion pipelines using Azure Data Factory.
• Managing and structuring data within Azure Data Lake Storage (Gen2).
• Data cleaning, transformation, and layer-based modeling with Azure Databricks and PySpark.
• Loading optimized data into Azure Synapse Analytics.

Tools & Technologies: Azure Data Factory, Azure Data Lake Storage (Gen2), Azure Databricks (with PySpark), Azure Synapse Analytics.

Key Skills: Real-time API data handling, data lake management, Spark-based transformations, data workflow orchestration, data warehousing, and performance tuning.

Project 3: Create Dynamic Mapping Data Flow in Azure Data Factory

Master the power of dynamic mapping data flows in Azure Data Factory. Learn to create flexible pipelines that adapt to changing data schemas.

• Understand the concepts of mapping data flows vs. traditional data flows.
• Create and configure dynamic mapping data flows.
• Utilize expressions and variables for dynamic mappings.
• Troubleshoot common issues.
• Process multiple files with varying schemas.

Project 4: Real-time Project using Metadata Driven Framework in Azure Data Factory

Take your ADF skills to the next level with a metadata-driven framework. Learn to design and implement pipelines that dynamically process multiple tables without code redesign.

• Understand the principles of a metadata-driven framework.
• Set up a metadata repository.
• Implement a metadata-driven pipeline in ADF.
• Optimize and troubleshoot the pipeline.

Project 5: Incremental Data Loading in the Cloud: A Hands-on Approach with Azure Data Factory and Watermarking

Implement efficient incremental data loading using Azure Data Factory and watermarking. This technique minimizes data processing time and resource consumption by only loading new or updated data.

• Set up a watermark table.
• Filter data using watermarking in ADF pipelines.
• Understand different types of incremental loads.
• Learn best practices for incremental loading.

Project 6: Auditing and Logging Data Pipelines in Azure: A Hands-on Approach

Implement a robust auditing and logging system for your Azure Data Factory pipelines.

• Capture pipeline execution details (start/end times, status, errors).
• Use stored procedures to query and analyze logs.
• Leverage system variables, activity outputs, and dynamic expressions for logging.

Refund Guarantee: We offer a 30-day money-back guarantee. If you're not satisfied, Udemy will refund your purchase (subject to Udemy's terms).

Who is this course for?

• Data Engineers
• ETL Developers
• Data Architects
• Anyone looking to enhance their Azure data engineering skills.