Databricks Data Engineer Associate: Practical Guide

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Overview

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This course covers all essential Databricks concepts for aspiring data engineers, including Apache Spark fundamentals, Delta Lake, performance optimization, and data pipeline creation. With real-world demonstrations, you will gain practical experience in deploying and orchestrating data engineering workflows. This course is designed to equip you with the skills needed to become a certified Databricks Data Engineer Associate. Starting with an introduction to Databricks, you will explore its role in modern data engineering and its integration with Apache Spark. From understanding basic data processing operations to building powerful data pipelines, you’ll gain hands-on experience in Spark architecture and execution, Delta Lake for data management, and the use of Databricks’ advanced features like Unity Catalog for governance and Delta Live Tables for orchestration. You’ll dive deep into Spark fundamentals, learning about data transformations, actions, and lazy evaluation, followed by the performance optimization techniques essential for data-heavy applications. The course also covers crucial data warehousing concepts like OLAP and OLTP, along with Delta Lake’s ACID transactions and time travel for data management. Through practical demonstrations, you will learn how to sign up for Databricks, create and manage notebooks, ingest and transform data, and optimize performance using partitions and parallelism. The course culminates in a comprehensive capstone project that ties everything together, giving you the experience and knowledge to pass the Databricks Certified Data Engineer Associate exam and excel in data engineering tasks. This course is designed for aspiring data engineers, data scientists, and IT professionals who wish to build a strong foundation in Databricks and Apache Spark. It's ideal for individuals looking to gain hands-on experience in data engineering workflows, Spark execution, performance optimization, and Delta Lake for managing large-scale data. Familiarity with data engineering concepts or programming basics will be helpful, but the course is suitable for both beginners and those seeking certification. The course follows a project-based approach, where you work through practical demonstrations and real-world scenarios. Each section introduces essential concepts followed by hands-on tutorials to reinforce learning. By the end, you will have mastered the skills necessary to build and optimize data pipelines and perform key tasks required for the Databricks Certified Data Engineer Associate exam. This course is based on Databricks Certified Data Engineer Associate - Practical Guide, by Yogesh Raheja, Thinknyx Technologies. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Course Introduction
- This module provides an overview of the course structure, key learning goals, and the skills students will develop. It outlines the expectations and introduces the core concepts that will be explored throughout the course.
Getting Started with Databricks
- 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.
Understanding Spark Fundamentals
- 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.
Understanding Spark Execution Basics
- 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.
Performance Basics
- 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.
Data Warehousing Fundamentals
- 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.
Delta Lake & Data Management
- 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.
Governance, BI & Pipelines
- 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.
Orchestration in Databricks
- 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.
Capstone Project
- 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.
Conclusion
- 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.