This module provides an introduction to Databricks, covering its core functionality, key features, and basic operations. Learners will gain hands-on experience with the Databricks interface, including creating notebooks and ingesting data. It also outlines the essential elements of the Databricks platform for data engineers.

This module provides an introduction to Apache Spark and PySpark, covering fundamental concepts, architecture, and practical applications. Learners will explore how to use Spark for data processing, understand its core components, and apply it in real-world scenarios using DataFrames, SQL, and Databricks magic commands.

This module explores the foundational concepts of Spark execution, including how to use the explain() function to analyze execution plans, the distinction between transformations and actions, and the impact of narrow versus wide transformations on performance. Learners will gain a clear understanding of Spark's lazy evaluation model and how to optimize data processing workflows.

This module explores key concepts in Spark performance optimization, including the role of partitions, parallelism, and the differences between repartition() and coalesce(). Learners will gain a solid understanding of how to manage and optimize data processing for efficiency.

This module provides a comprehensive overview of data warehousing, covering essential concepts such as OLTP vs. OLAP, data warehouse architecture, and performance optimization techniques. Learners will also explore platforms like Databricks and understand how they support analytics workloads. The module equips students with foundational knowledge needed to design and manage efficient data warehousing solutions.

This module covers the fundamentals of Delta Lake, including its role in data management, ACID transactions, time travel, and the Medallion Architecture. Learners will explore real-world applications, data preparation techniques, and integration strategies with external data sources like S3. The module also emphasizes best practices for reliable and scalable data pipeline management.

This module covers the essentials of data governance, business intelligence, and data pipelines within the Databricks platform. Learners will gain hands-on understanding of how to manage data flow, ensure governance, and derive insights using tools like Unity Catalog and Delta Live Tables. The module emphasizes practical implementation and best practices for data workflows.

This module provides an introduction to orchestration in Databricks, covering key concepts such as job automation, GitHub integration, and pipeline management. Learners will gain practical skills in setting up and scheduling data workflows to improve efficiency and collaboration in data processing tasks.

This module guides learners through the process of completing a capstone project, focusing on building and analyzing a comprehensive data pipeline using Databricks. It covers essential steps such as data ingestion, transformation, and visualization, as well as the implementation of the medallion architecture.

This module provides a comprehensive summary of the course, highlighting key concepts and skills essential for becoming a certified data engineer. Learners will reflect on their progress and reinforce core principles through a structured review.