Real-World Project on Formula1 Racing using Azure Databricks, Delta Lake, Unity Catalog, and Azure Data Factory [DP203]
Rating: Bestseller | Students: 114,652+ | Last Updated: November 2024
Description
Looking to kickstart or advance your career in data engineering? This course is your ultimate guide to mastering Azure Databricks and Spark Core. Designed for both beginners and experienced professionals, the course offers a hands-on learning experience that revolves around a real-world Formula1 motor racing project. You won’t just learn the theory—you’ll build a complete data engineering solution step by step.
Imagine being able to work with big data on the cloud, analyze and transform complex datasets, and create powerful visualizations to drive business decisions. With Azure Databricks, Spark, Delta Lake, and Unity Catalog as your tools, this course helps you gain skills that are in high demand across industries. By the end of this course, you’ll be able to confidently design and implement robust data pipelines, enabling data-driven strategies in any organization.
This course is structured to teach practical skills, focusing on real-world applications. Learn how to implement Lakehouse solutions using Delta Lake, gain insights into Unity Catalog for data governance, and build Azure Data Factory pipelines for automation. It's perfect for anyone looking to transition into data engineering or expand their existing skills with modern cloud technologies.
What You'll Learn
- Build a real-world data project using Azure Databricks and Spark Core.
- Learn data engineering skills in Delta Lake, Azure Data Lake Gen2, and Azure Data Factory (ADF).
- Create notebooks, dashboards, clusters, and jobs in Azure Databricks.
- Ingest and transform data using PySpark and Spark SQL in Databricks.
- Implement Lakehouse architecture using Delta Lake.
- Create Azure Data Factory pipelines and triggers for scheduling and monitoring.
- Connect Azure Databricks to Power BI for reporting.
- Understand Unity Catalog and data governance capabilities for Data Lakehouse solutions.
Who This Course Is For
- University students seeking a career in Data Engineering.
- IT developers transitioning to Data Engineering roles.
- Data Engineers working on on-premises technologies or other cloud platforms (AWS, GCP).
- Data Architects wanting to learn Azure Data Engineering solutions.
Requirements
- Basic knowledge of IT and databases.
- Access to an Azure account for hands-on practice (Azure Student or Corporate subscription preferred).
Key Technologies Covered
Azure Databricks
- Databricks notebooks, clusters, and workflows.
- PySpark for data ingestion and transformations.
- Spark SQL for creating databases, tables, and performing transformations.
Delta Lake
- Data Lakehouse architecture implementation.
- Incremental loads and time travel.
- Converting Parquet files to Delta format.
Unity Catalog
- Data governance and 3-level namespace structure.
- Mini-project to explore data discovery and audit features.
Azure Data Factory
- Pipeline creation and monitoring.
- Handling errors and missing files.
- Scheduling with Data Factory triggers.
Final Thoughts
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