Data Engineering in Databricks - Course Overview

Data Engineering in Databricks is a comprehensive course designed to equip participants with the essential skills and knowledge to excel in the field of data engineering using the powerful Databricks platform. As data continues to play a pivotal role in modern businesses, the ability to efficiently manage and process data has become crucial for success. 

This hands-on course takes participants on a journey through the fundamental concepts and best practices of data engineering, with a specific focus on the Databricks Medallion Architecture. Throughout the course, participants will gain a deep understanding of how to ingest, process, and store data using Bronze, Silver, and Gold Tables within the Delta Lake framework. 

After completing Data Engineering Databricks Training, students will be able to:

  • Efficiently design and implement data pipelines using Databricks Medallion Architecture.
  • Understand and apply best practices for data quality enforcement and Slowly Changing Dimension (SCD) tables.
  • Leverage the power of Streaming design patterns for real-time data processing.
  • Perform data transformations and aggregations using SQL and other data processing techniques.
  • Work with multi-hop pipelines to optimize data flow and storage.
  • Effectively propagate updates and deletes using Change Data Feed and Partition Boundaries.
  • Orchestrate and schedule multi-task jobs for seamless data processing and automation.
  • Collaborate with Repos to promote code and manage workloads efficiently.
  • Implement best practices for designing and maintaining data storage solutions in Databricks.
  • Contribute to data-driven decision-making processes and drive insights from large datasets.

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Key Features of Data Engineering in Databricks Course

  • Understand the Databricks Medallion Architecture and its significance in data engineering.
  • Learn the process of ingesting data into Bronze Tables and the concept of Multiplex Bronze.
  • Master the techniques for promoting data to Silver Tables and implementing Slowly Changing Dimension Tables.
  • Gain expertise in creating Gold Tables and working with the Query Layer to optimize data retrieval.
  • Ensure data security and compliance with a focus on managing PII (Personally Identifiable Information).
  • Propagate updates and deletes effectively using Change Data Feed and Partition Boundaries.
  • Acquire essential skills in orchestrating and scheduling multi-task jobs with Repos.
  • Benefit from hands-on lab sessions, where real-world scenarios will be simulated to reinforce learning.

Who can participate in the Data Engineering in Databricks Training?

  • Data engineers
  • Data analysts
  • Data scientists
  • Professionals involved in designing, building, and maintaining data pipelines.
  • Professionals working on data storage solutions.
  • Anyone looking to work in the field of data engineering.
  • Those seeking to leverage the power of Databricks for efficient data processing.

What are the prerequisites for the Databricks Data Engineering?

  • Participants should have a basic understanding of data concepts, databases, and SQL. Familiarity with data processing and programming fundamentals will be beneficial, but not mandatory.

Advantages of Learning & Earning Databricks Data Engineering Certification

  • Acquire in-demand skills in data engineering using one of the most popular cloud-based platforms.
  • Gain hands-on experience through practical lab sessions, enabling immediate application of learned concepts in real-world projects.
  • Enhance career prospects with industry-recognized certification in Data Engineering from a reputable platform like Databricks.
  • Stay ahead in the competitive job market and contribute to data-driven decision-making in organizations.

Why opt for CloudThat for Learning Data Engineering with Databricks?

  • The Data Engineering in Databricks training is thoughtfully designed to cater to individual needs, allowing participants to interact with experienced trainers and professionals from various organizations.
  • This comprehensive training spans 8 hours, and the flexible scheduling option ensures convenience for all participants, enabling them to balance their learning with their existing commitments.
  • The learning path encompasses all core concepts related to the fundamentals of data engineering in Databricks. Participants will gain insights into different cloud providers, data management, governance, and more.
  • Our course is led by industry-leading trainers, each possessing extensive experience in the field of data engineering, guaranteeing top-notch learning and valuable insights.
  • Throughout the training, participants will engage in practical exercises and real-life case studies, tackling industry-level questions for a thorough understanding of Databricks data engineering principles.
  • With hands-on exposure to the Azure Cloud Environment, participants will gain valuable skills, empowering them to excel in data engineering projects in the Databricks platform.

Modules covered in Databricks Data Engineering Course Download Course Outline

  • Explain Delta Lake and its importance in Databricks Medallion Architecture.
  • Introduce data concepts related to Databricks Medallion Architecture.
  • Describe the multi-hop pipeline and define Bronze, Silver, and Gold Tables.
  • Include details about the lab activity: Streaming design patterns.

  • Discuss the process of ingesting data into the bronze table.
  • Explain Multiplex Bronze and its significance.
  • Include details about the lab activities: Auto Load from Multiplex Bronze, Streaming from Multiplex Bronze.

  • Describe the process of promoting data from Bronze to Silver Table.
  • Explain Quality Enforcement and Slowly Changing Dimension Table concepts.
  • Include details about the lab activities: Streaming Static Join, Type 2 SCD, Static Stream Join.

  • Introduce the Query Layer and its role.
  • Include details about the lab activities: Stored Views, Materialized Gold Tables.

  • Discuss PII (Personally Identifiable Information) and Regulatory Compliance.
  • Explain how to manage ACLs (Access Control Lists).
  • Include details about the lab activities: PII Lookup Table, Storing PII Securely, Deidentified PII Access.

  • Explain the Change Data Feed concept.
  • Describe Partition Boundaries and their importance.
  • Include details about the lab activities: Processing records from Change Data Feed, Propagating deletes with Change Data Feed, Deleting at Partition Boundaries.

  • Discuss Orchestration and scheduling of Multi-task Jobs.
  • Explain how to promote code with Repos.
  • Include details about the lab activities: Multi-task Jobs, CLI and Rest API, Deploying Workloads.


    • This course helps in clearing Databricks Data Engineering Associate certification exam.
    • Showcases proficiency in building scalable data pipelines and processing multi-hop data flows
    • Demonstrates competence in ensuring secure data storage with Databricks Medallion Architecture.
    • Enhances credibility and career prospects as a skilled data engineer.

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Course ID: 16112

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Frequently Asked Questions

Data Engineers with Databricks expertise can earn competitive salaries, varying based on experience and location.

Data Engineers should have expertise in programming languages like Python or Scala, SQL querying, and big data technologies.

Yes, the course is designed for both beginners and experienced professionals seeking to excel in Databricks data engineering.

This course provides a specific focus on data engineering using Databricks Medallion Architecture, a powerful platform for big data processing.

Absolutely! The course includes practical exercises and case studies to address industry-level data engineering challenges.

The course offers flexible learning options, including both online and in-person.

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