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Databricks Certified Data Engineer Associate Crash Course

Published by Pearson

Intermediate content levelIntermediate

Gain expertise in managing, accessing, and optimizing data, and prepare for exam success

  • Exam-focused training: Tailored specifically to the Databricks Data Engineer Associate certification exam, ensuring learners are exam-ready with confidence
  • Efficient skill-building: Condenses months of learning into a 5-hour crash course, maximizing time without sacrificing depth
  • Hands-on practice: Offers practical, real-world scenarios mirroring exam questions to solidify understanding and application

Join tech expert Tim Warner for an intensive crash course meticulously designed to prepare you to pass the Databricks Certified Data Engineer Associate exam. The course has been crafted not just to impart essential data engineering skills but to ensure that learners are thoroughly prepared to pass the certification exam. Through a blend of expert-led instruction and hands-on demonstrations, learners will dive into the core components of the Databricks Lakehouse Platform, including Apache Spark, SQL, and Python, focusing on real-world applications and exam-specific scenarios.

The importance of this live course lies in its dual objective: to equip learners with in-demand data engineering skills and to ensure they possess the knowledge and confidence to achieve Databricks certification. In today's data-driven world, proficiency in Databricks offers a competitive edge, opening doors to advanced career opportunities in data engineering. This course stands out by offering a concise yet comprehensive pathway to certification, making it an ideal investment for professionals seeking to enhance their credentials and expertise swiftly and effectively.

What you’ll learn and how you can apply it

  • Design efficient data engineering solutions: Grasp the fundamentals of the Databricks Lakehouse Platform, enabling the creation of efficient data engineering solutions that are scalable and optimized for performance.
  • Implement ETL processes using Apache Spark: Develop the ability to use Apache Spark for extracting, transforming, and loading (ETL) data, crucial for handling large datasets efficiently within the Databricks environment.
  • Operationalize data engineering workflows: Learn to operationalize data engineering workflows by deploying ETL pipelines and Databricks SQL queries and dashboards into production, ensuring data is accurate, timely, and accessible.

This live event is for you because...

  • You’re a data engineer, software developer, or IT professional aiming to leverage Databricks for scalable data engineering and validate skills for certification
  • You're a beginner with a basic understanding of data engineering concepts or a more advanced learner looking to formalize your expertise. You want the knowledge and skills to pass the Databricks Certified Data Engineer Associate exam as well as to apply Databricks solutions to real-world data challenges

Prerequisites

  • Basic understanding of Databricks: Familiarity with the Databricks Lakehouse Platform, including its architecture and capabilities, will be beneficial.
  • Programming knowledge: Proficiency in Apache Spark, SQL, and Python is recommended, as these are key components of the Databricks ecosystem and will be extensively covered during the course.

Course Setup

  • Development environment: Have a modern web browser (Chrome, Firefox, Safari, or Edge) ready for accessing the Databricks workspace. No specific IDE is required since we will be using Databricks notebooks.
  • Software requirements: Install Python 3.x on your local machine for any local development or testing. While most exercises will be performed directly in Databricks notebooks, having Python installed locally is beneficial for understanding the code and for any local dataset manipulation.
  • GitHub repository: All course materials, including datasets and notebooks, will be provided through one of my public GitHub repositories: https://github.com/timothywarner/databricks
  • Suggested: Databricks account: You can sign up for a Databricks Community Edition account at https://community.cloud.databricks.com/. This edition is free and provides access to a micro-cluster and a cluster manager to run your code.

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Lesson 1: Databricks Lakehouse Platform Overview (45 minutes)

  • Understanding the Lakehouse architecture
  • Exploring Databricks workspace and its components
  • Q&A

5-minute break

Lesson 2: Data Engineering with Apache Spark (45 minutes)

  • Extracting, transforming, and loading data using Apache Spark
  • Implementing batch and stream processing
  • Q&A

5-minute break

Lesson 3: Incremental Data Processing with Delta Lake (45 minutes)

  • Managing ACID transactions and data versioning
  • Implementing time travel and Z-ordering for performance optimization
  • Q&A

5-minute break

Lesson 4: Developing Production Pipelines (45 minutes)

  • Constructing and managing job workflows
  • Setting up alerts and monitoring job performance
  • Q&A

5-minute break

Lesson 5: Data Governance and Security (45 minutes)

  • Implementing data object access control
  • Best practices for securing Databricks environments
  • Q&A

5-minute break

Lesson 6: Query Optimization and Performance Tuning (50 minutes)

  • Utilizing data skipping and Z-ordering for query acceleration
  • Best practices for optimizing Spark SQL queries
  • Q&A and final wrap-up

Your Instructor

  • Tim Warner

    Tim Warner has been a Microsoft MVP in Azure AI and Cloud/Datacenter Management for 6 years and a Microsoft Certified Trainer for more than 25 years. His O'Reilly Live Training classes on generative AI, GitHub, DevOps, data engineering, cloud computing, and Microsoft certification reach hundreds of thousands of students around the world. He's written for Microsoft Press, presented at Microsoft Ignite, and contributed to several Microsoft open-source projects. You can connect with Tim on LinkedIn: timw.info/li.

Skill covered

Data Engineering