Databricks Certified Data Engineer Professional - Preparation Course

What You'll Learn

In this course, you'll gain the essential knowledge and practical skills to excel in the Databricks Certified Data Engineer Professional exam. The course is structured to provide a deep understanding of the core tools and concepts that are critical for a successful career as a data engineer on the Databricks platform.

Throughout the course, you’ll focus on building and deploying data solutions, using Databricks Lakehouse architecture, mastering the Delta Lake API, and optimizing workflows to meet the demands of modern data engineering. More specifically, you’ll learn how to:

• Model data management solutions effectively using Databricks Lakehouse architecture, including bronze, silver, and gold tables, views, and the physical layout.
  
  

• Build efficient data processing pipelines using the Spark and Delta Lake APIs, including batch and incremental ETL processing.
  
  

• Use the Databricks platform tools like Databricks CLI and REST API for deploying, managing, and triggering production jobs.
  
  

• Deploy production pipelines with best practices in security, governance, and data privacy.
  
  

• Monitor and log production jobs to ensure smooth performance and error-free data pipelines.
  
  

• Implement best practices for coding and deployment, including cluster management, permissions, and code orchestration.

By the end of this course, you will be fully equipped with the knowledge and skills to confidently tackle the Databricks Certified Data Engineer Professional exam.

Requirements

To get the most out of this course, you should already have a strong understanding of the Databricks platform at the associate level. This course builds upon core concepts, so having experience with the foundational features of Databricks Lakehouse, including basic data processing and management principles, is crucial. The knowledge you’ve gained from earlier stages of learning will help you grasp the more advanced concepts and practices taught here.

If you're not yet familiar with these concepts, we highly recommend completing the Databricks Associate Data Engineer certification preparation course before diving into this professional-level material. The associate-level course covers essential topics like basic data architecture, data pipelines, data ingestion, and transformations within the Databricks environment, providing a solid base for tackling more complex scenarios.

The associate-level course covers all the essential topics you’ll need to be comfortable with, such as:

• Data Architecture: Understanding the basic structure of Databricks Lakehouse.
  
  

• Basic Data Processing Pipelines: Getting hands-on with simple ETL processes.
  
  

• Foundational Data Management Principles: Building a strong understanding of how to store, access, and transform data.

By completing the associate course first, you’ll be equipped to tackle the more advanced topics introduced in this professional-level course.

Although Spark and Delta Lake knowledge are not mandatory to start this course, having a basic understanding or prior experience with these tools will be highly beneficial. Familiarity with these technologies will make it easier to grasp the more complex concepts and practical exercises covered in the professional certification exam material.

In summary, if you are already comfortable with Databricks at the associate level and have a general understanding of Spark and Delta Lake, you're well-prepared to dive into the material of this course. However, if you haven’t yet covered the fundamentals, we strongly recommend that you complete the associate-level course before enrolling here. This will ensure that you can get the most out of your learning experience and be well-prepared for the Databricks Certified Data Engineer Professional exam.

Course Description

If you're aiming for the Databricks Certified Data Engineer Professional certification, this course is designed specifically to guide you through every step of the preparation process. Whether you are looking to gain the certification or enhance your practical data engineering skills, this course will provide you with a comprehensive understanding of the Databricks platform, ensuring you are well-prepared to build and manage advanced data engineering solutions.

The course takes a hands-on approach to ensure that you are not only prepared for the certification exam but also gain real-world skills that are highly valuable in today's data engineering field. By diving deep into the core features and functionalities of the Databricks Lakehouse, you will develop the ability to design, implement, and optimize both batch and real-time data pipelines. You will explore how Databricks combines the flexibility and scalability of data lakes with the power and performance of data warehouses, giving you the best of both worlds when it comes to building modern data architectures.

As you progress through the course, you will tackle a range of practical use cases and real-world projects that simulate the challenges faced by data engineers. You will learn how to handle large-scale data ingestion, perform complex data transformations, and implement effective data workflows that are capable of running at scale. One of the main focuses of the course is understanding and implementing Lakehouse architecture, which allows you to handle data from various stages—raw, intermediate, and business-ready data—while maintaining seamless integration and high performance.

In this course, you will gain practical experience with tools like Delta Lake, Apache Spark, and the Databricks CLI. You’ll build pipelines that utilize these tools and learn how to handle data from ingestion to transformation, and finally, to analysis. Moreover, the course covers best practices for ensuring data quality, security, and governance, which are key concerns in any large-scale data environment.

In addition to technical skills, this course emphasizes critical aspects of production data pipelines, such as monitoring, logging, and troubleshooting, so you are ready to keep your data operations running smoothly and efficiently. You’ll also delve into advanced topics such as automating workflows, job orchestration, and using the Databricks REST API, ensuring that you can manage your pipelines in an automated, production-grade environment.

By completing this course, you'll be well-equipped with the expertise necessary to sit for the Databricks Certified Data Engineer Professional exam. But more importantly, you’ll acquire the skills and confidence to build robust, optimized, and secure data pipelines that can be implemented in real-world enterprise environments. The tools, techniques, and best practices you will learn are directly applicable to the types of tasks and challenges data engineers face in the field, making this course an invaluable resource for professionals seeking to enhance their careers in data engineering.

This course goes beyond just preparing you for the exam—it aims to make you a proficient and confident Databricks user. It’s perfect for those who are looking to elevate their skills in the Databricks ecosystem, from managing workflows and pipelines to ensuring data security and compliance. Through practical exercises, detailed tutorials, and insightful demonstrations, you will gain an in-depth understanding of the platform and the essential skills required to succeed in any professional data engineering role.

Upon completion, you’ll not only be fully prepared to pass the certification exam but also ready to implement sophisticated, scalable, and secure data solutions on the Databricks platform in any enterprise setting. Whether you’re new to Databricks or looking to deepen your existing knowledge, this course will guide you step-by-step toward mastering the platform and becoming a certified Databricks Data Engineer Professional.

Modeling Data Management Solutions

• Lakehouse Architecture: Learn how to structure your data in the Databricks Lakehouse, utilizing the bronze, silver, and gold layers to manage data throughout different stages of processing. You’ll get hands-on experience with designing scalable architectures that help store raw data, intermediate datasets, and curated, business-ready data in one unified platform.
  
  

• Data Modeling Concepts: Dive into essential data modeling principles, such as constraints, lookup tables, and slowly changing dimensions (SCDs). You’ll also learn how to create and manage data tables and views to organize your data efficiently, making it easy to access, query, and update.
  
  

• Data Organization and Structure: Master the techniques for managing large datasets effectively, ensuring they are accessible, manageable, and properly indexed.

Building Data Processing Pipelines

• Batch Processing with Spark API: Learn to use the Spark API for creating batch ETL pipelines. Understand the underlying architecture and how to perform efficient data extraction, transformation, and loading (ETL) operations for large datasets.
  
  

• Incremental Processing with Delta Lake: Discover how to build incremental ETL pipelines using Delta Lake. Learn advanced techniques such as upserts and merge operations, which allow you to efficiently handle data updates, deletions, and new records while ensuring consistency and performance.
  
  

• Data Deduplication and CDC: Master techniques for deduplicating data and using Change Data Capture (CDC) to propagate updates across your data pipelines automatically, keeping your data accurate and up-to-date without requiring manual intervention.
  
  

• Partitioning for Performance: Learn how to partition large datasets to improve pipeline performance. Discover best practices for partitioning data and how to optimize partition sizes to reduce read and write times in large-scale datasets.

Using the Databricks Platform

The Databricks platform provides a comprehensive suite of tools for managing and executing data pipelines at scale, whether they are batch, real-time, or machine learning workflows. Understanding how to use these tools effectively is critical for building and managing production-grade systems. In this section, we’ll focus on two essential tools in the Databricks ecosystem: the Databricks Command Line Interface (CLI) and the Databricks REST API. Mastery of these tools will enable you to automate, deploy, and monitor your data workflows with precision and efficiency.

Databricks CLI

The Databricks Command Line Interface (CLI) is a powerful tool that allows you to interact with Databricks directly from your terminal. It provides a convenient way to automate the management of Databricks resources, enabling you to work more efficiently when managing notebooks, clusters, jobs, and other assets. By using the CLI, you can streamline your workflows and integrate Databricks more seamlessly into your DevOps pipeline.

In this section, you'll gain proficiency in several key CLI commands, such as:

• Managing Notebooks: Learn how to deploy, export, and manage notebooks directly from the command line. This is essential for automating notebook-based workflows and integrating them into your CI/CD pipeline for faster and more reliable deployments.
  
  

• Cluster Management: Gain hands-on experience managing clusters from the CLI, including creating, deleting, and scaling clusters. You'll also learn how to monitor cluster health and performance metrics without having to access the Databricks UI.
  
  

• Job Scheduling and Orchestration: Discover how to schedule and manage your jobs from the CLI. By automating job execution, you can run pipelines on-demand, at scheduled intervals, or in response to external triggers, allowing for a more efficient workflow.
  
  

• Managing Secrets and Configuration: Learn how to securely manage secrets and other sensitive information within the Databricks platform using the CLI. This will help you ensure compliance with security best practices while working with data at scale.

Through hands-on exercises, you’ll learn how to leverage the Databricks CLI to automate your workflows and simplify your day-to-day tasks, making it an invaluable tool for any data engineer working in the Databricks environment.

Implementing Security and Governance

• Access Control with ACLs: Explore best practices for securing your Databricks environment, including managing user access to clusters, jobs, and notebooks using Access Control Lists (ACLs).
  
  

• Data Protection and Privacy: Learn how to implement security at the data level by creating dynamic views that control access to sensitive data. Understand how to comply with GDPR and CCPA by securely deleting data when necessary.
  
  

• Securing Production Workflows: Ensure that your production data pipelines are secure by following industry best practices for data access, encryption, and auditing.

Monitoring and Logging Production Jobs

• Job Monitoring and Logging: Learn how to set up comprehensive monitoring for your Databricks jobs. You’ll be trained in the process of logging metrics, tracking performance, and identifying bottlenecks or issues within your production pipelines.
  
  

• Debugging and Alerts: Develop skills in debugging and troubleshooting issues within your data pipelines. Learn how to set up alerts for job statuses, errors, and performance issues, allowing you to proactively manage and resolve production problems before they affect your workflows.

Deploying and Testing Code

• Best Practices for Code Management: Discover best practices for managing, testing, and deploying your Databricks code in production environments. You'll learn techniques for using version control, automating deployment workflows, and managing code across teams.
  
  

• Job Scheduling and Orchestration: Understand how to schedule jobs to run at specific times or intervals, and how to orchestrate complex workflows with job dependencies. This ensures that your data processing tasks are executed reliably and efficiently.
  
  

• Optimizing Workflows: Dive into strategies for optimizing your data processing workflows, focusing on reducing costs, improving performance, and scaling your pipelines for large datasets.

This course goes beyond just preparing you for the certification exam. It provides you with the practical skills needed to design and manage robust data pipelines in a real-world, production environment. You’ll gain deep, hands-on knowledge of the Databricks Lakehouse, Delta Lake, Spark, and other key tools used by professional data engineers today.

By the end of the course, you will be fully equipped to:

• Build and manage production-grade data pipelines.
  
  

• Implement best practices for security, governance, and optimization.
  
  

• Master the Databricks platform, from the CLI and REST API to security practices and monitoring.
  
  

• Successfully pass the Databricks Certified Data Engineer Professional exam, with confidence in your ability to work on enterprise-scale data solutions.

Who This Course Is For

This course is designed for individuals seeking to earn the Databricks Certified Data Engineer Professional certification. It is particularly beneficial for:

• Junior Data Engineers: If you're currently working at the associate level and want to advance your skills to the professional tier, this course will help you bridge the gap. You'll learn the advanced techniques and tools that will take your data engineering career to the next level.
  
  

• Data Engineers on the Databricks Platform: If you’re already working as a data engineer on Databricks, but feel you lack the advanced skills needed to implement large-scale, production-ready data pipelines, this course is tailored to help you master these crucial aspects of data engineering. You’ll gain the hands-on experience required to work with high-performance data pipelines.
  
  

• Data Professionals Focused on Security and Governance: This course is also ideal for data professionals interested in understanding and implementing best practices around security, governance, and deployment of data engineering solutions. You'll gain a solid understanding of how to maintain data privacy, secure sensitive data, and comply with regulatory standards while working on the Databricks platform.
  
  

• Experienced Databricks Associate-Level Users: If you’ve been working with Databricks at the associate level and are looking to build upon your knowledge, this course is designed to help you level up. You’ll deepen your expertise in key areas like Delta Lake, Spark, and production pipelines, while preparing for the certification exam that will validate your advanced skills.

Whether you’re just starting to climb the Databricks ladder or aiming to solidify your place as a professional data engineer, this course will help you develop the knowledge, skills, and confidence needed to excel in a real-world Databricks environment.

What You'll Gain

By the end of this course, you will:

• Understand Databricks Lakehouse Architecture: You’ll gain a comprehensive understanding of the Databricks Lakehouse architecture, including its components and how it integrates with modern data engineering workflows. You'll learn the fundamental concepts behind Lakehouse models and how they facilitate data storage, processing, and management in scalable, high-performance environments.
  
  

• Build Production-Ready Data Pipelines: You will learn how to design, build, and optimize production-grade data pipelines using Delta Lake and Spark APIs. This includes the full lifecycle of data processing, from batch ETL to real-time streaming workflows. You’ll become proficient in the advanced features of Delta Lake, like handling slowly changing dimensions, change data capture (CDC), and incremental data processing.
  
  

• Master Security Best Practices: Data security is a core focus of the course. You’ll gain hands-on experience implementing security best practices for Databricks, ensuring your pipelines are compliant with industry standards like GDPR and CCPA. You will learn how to manage access controls, encryption, and secure data deletion to protect sensitive information in production environments.
  
  

• Leverage Monitoring and Logging Tools: You’ll be trained to configure and use various monitoring tools to keep track of the performance and reliability of your data pipelines. You will also learn how to implement log management practices to monitor, debug, and resolve issues effectively. This will ensure your production systems run smoothly and errors are detected and corrected in real time.
  
  

• Be Fully Prepared for the Certification Exam: This course is designed to ensure that you’re not just learning the concepts, but also applying them in practical scenarios. By the end, you’ll be ready to sit for the Databricks Certified Data Engineer Professional exam, equipped with the knowledge, hands-on experience, and best practices to pass with confidence.
  
  

• Implement Workflow Optimization and Best Practices: You will learn to optimize your data workflows to improve performance, cost-efficiency, and scalability. In addition, you will master the best practices for managing production workloads, including how to efficiently scale your infrastructure and improve the reliability of your data pipelines.

With this knowledge and hands-on experience, you will be well-positioned to excel in your role as a professional data engineer, and successfully pass the Databricks Certified Data Engineer Professional exam.

Enroll Today

Take the next step in your data engineering journey by enrolling in this in-depth, practical preparation course for the Databricks Certified Data Engineer Professional exam. Whether your goal is to earn a recognized certification, sharpen your skills for a current role, or prepare for a new opportunity, this course provides the tools, insights, and hands-on experience to help you succeed.

The course is structured to not only prepare you for the exam but to build real-world confidence in designing and deploying scalable data pipelines, working with Delta Lake and Spark APIs, and implementing robust governance and monitoring strategies on the Databricks platform.

From day one, you’ll gain access to:

• Real-world demonstrations and practical labs
  
  

• Interactive exercises and quizzes that reinforce your learning
  
  

• Expert guidance and best practices from experienced instructors
  
  

• The latest content updated in line with the 2025 exam requirements

If you're serious about becoming a Databricks Data Engineer Professional, this is the course that will get you there. You’ll walk away with the ability to manage production-grade data workflows and the confidence to pass one of the most respected certifications in the industry.

Enroll now and unlock access to all the content, updates, and expert-led instruction that will empower you to take your career in data engineering to the next level.