Implement Azure Databricks Data Engineering Solutions for Secure and Scalable Lakehouse Platforms

This course teaches data engineers how to design, secure and manage Azure Databricks environments using Unity Catalog, Delta Lake, Lakeflow Jobs and Azure security services. Participants learn how to build ingestion pipelines, govern data access, optimise workloads and deploy production-ready lakehouse solutions.

  • Why get trained: Learn how to configure Azure Databricks, secure Unity Catalog, build Delta Lake pipelines and automate workloads using Lakeflow Jobs, SQL and Python.
  • Why it matters: Azure Databricks and Unity Catalog skills help organisations improve data governance, accelerate analytics and deliver more scalable, secure and cost-effective data platforms.
  • Who should attend: Data engineers, ETL developers and data platform professionals with experience in SQL, Python, Azure Databricks and cloud-based data engineering.

Build enterprise-grade data engineering skills and prepare for the Microsoft Certified: Azure Databricks Data Engineer certification. HRD Corp Claimable.

Overview

Master end-to-end data engineering with Azure Databricks and Unity Catalog.

This course moves from foundational setup to production deployment, covering environment configuration and enterprise-grade governance. Learn to build robust ingestion pipelines, implement security with Unity Catalog, and deploy optimized workloads.

By the end, you will have the practical skills to implement, secure, and maintain scalable lakehouse solutions that meet rigorous enterprise requirements.

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Skills Covered

  • Set up and configure an Azure Databricks environment
  • Secure and govern Unity Catalog objects in Azure Databricks
  • Prepare and process data with Azure Databricks
  • Deploy and maintain data pipelines and workloads with Azure Databricks

Prerequisites

Learners are expected to have a good understanding of Azure Databricks workspaces and Unity Catalog, along with familiarity with data access patterns and core data engineering and data warehouse concepts.

In addition, they should have foundational knowledge of Azure security, including Microsoft Entra ID, and be familiar with Git version control fundamentals.

Target Audience

The target audience is data engineers who have fundamental knowledge of data analytics concepts, a basic understanding of cloud storage, and familiarity with data organization principles.

They should be comfortable working with SQL and have experience using Python, including notebooks, for data engineering tasks.

Course Curriculum

Module 1: Set up and configure an Azure Databricks environment

Build a solid foundation in Azure Databricks by understanding its architecture, integrations, compute options, and data organization capabilities. Learn how Azure Databricks provides a unified platform for data engineering, analytics, and AI workloads in the cloud.

  • Explore Azure Databricks
  • Understand Azure Databricks architecture
  • Understand Azure Databricks Integrations
  • Select and Configure Compute in Azure Databricks
  • Create and organize objects in Unity Catalog

Module 2: Secure and govern Unity Catalog objects in Azure Databricks

Unity Catalog provides centralized governance and security for data assets in Azure Databricks. This module explores how to secure Unity Catalog objects through access control strategies, fine-grained permissions, credential management, and authentication mechanisms.

You’ll learn how to implement table and schema-level security, enforce row and column filtering, securely access secrets from Azure Key Vault, and authenticate data access using service principals and managed identities.

  • Secure Unity Catalog objects
  • Govern Unity Catalog objects

Module 3: Prepare and process data with Azure Databricks

Master the essential skills to build robust, scalable data engineering solutions with Azure Databricks and Unity Catalog. Learn to design effective data models, ingest data from diverse sources, transform raw data into analytics-ready formats, and ensure data quality across your lakehouse architecture.

  • Design and implement data modeling with Azure Databricks
  • Ingest data into Unity Catalog
  • Cleanse, transform, and load data into Unity Catalog
  • Implement and manage data quality constraints with Azure Databricks

Module 4: Deploy and maintain data pipelines and workloads with Azure Databricks

Master the complete lifecycle of building, deploying, and maintaining production-ready data pipelines in Azure Databricks—from design and orchestration to monitoring and optimization.

  • Design and implement data pipelines with Azure Databricks
  • Implement Lakeflow Jobs with Azure Databricks
  • Implement development lifecycle processes in Azure Databricks
  • Monitor, troubleshoot and optimize workloads in Azure Databricks

 

Dates & Locations

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July 6, 2026 - July 9, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3000

July 6, 2026 - July 9, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 3000

August 3, 2026 - August 6, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3000

August 3, 2026 - August 6, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 3000

October 5, 2026 - October 8, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3000

October 5, 2026 - October 8, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 3000

November 2, 2026 - November 5, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: GTR
Exam:
RM 374
RM 3000

November 2, 2026 - November 5, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 3000
Trainocate exam and cert

Exam & Certification

Microsoft Certified: Azure Databricks Data Engineer Associate

Demonstrate expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks.

  • Level: Intermediate
  • Product: Azure Databricks
  • Role: Data Engineer
  • Subject: Data engineering

Training & Certification Guide

As a candidate for this Microsoft Certification, you should have subject matter expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks. You should also have experience applying data quality and data governance best practices in Unity Catalog.

You need to know how to ingest and transform data by using Structured Query Language (SQL) and Python. You need experience with software development lifecycle (SDLC) practices, including Git. Additionally, you should be familiar with Microsoft Entra, Azure Data Factory, and Azure Monitor.

As a candidate for this Microsoft Certification, you should have subject matter expertise in integrating and modeling data, building and deploying optimized pipelines, and troubleshooting and maintaining workloads in Azure Databricks. You should also have experience applying data quality and data governance best practices in Unity Catalog.

You need to know how to ingest and transform data by using Structured Query Language (SQL) and Python. You need experience with software development lifecycle (SDLC) practices, including Git. Additionally, you should be familiar with Microsoft Entra, Azure Data Factory, and Azure Monitor.

Your responsibilities for this role include:

  • Setting up and configuring an Azure Databricks environment.
  • Securing and governing Unity Catalog objects.
  • Preparing and processing data.
  • Deploying and maintaining data pipelines and workloads.

You work closely with administrators, platform architects, solution architects, data scientists, and data analysts to design, deploy, and secure data engineering solutions by using Azure Databricks.

  • Set up and configure an Azure Databricks environment (15–20%)
  • Secure and govern Unity Catalog objects (15–20%)
  • Prepare and process data (30–35%)
  • Deploy and maintain data pipelines and workloads (30–35%)

Frequently Asked Questions

DP-750T00 teaches you how to build scalable data engineering solutions using Azure Databricks.

This course focuses on end-to-end data engineering, from setting up environments to deploying production-ready pipelines. It emphasizes building lakehouse architectures using Azure Databricks and Unity Catalog.

Key learning areas:

  • Data ingestion and transformation
  • Building ETL/ELT pipelines
  • Data governance with Unity Catalog
  • Deploying scalable lakehouse solutions

Pro Tip: Focus on understanding the full data lifecycle, not just pipelines. Employers look for engineers who can design end-to-end systems.

This course is designed for data engineers and professionals working with data platforms.

It is ideal for individuals who already understand data fundamentals and want to build scalable solutions using Azure Databricks.

Best suited for:

  • Data engineers
  • ETL developers
  • Cloud data professionals
  • Analytics engineers

Pro Tip: If you already work with SQL or Python, this course can help you transition into advanced cloud data engineering roles.

You will learn to design, build, and manage data pipelines using Azure Databricks.

The course emphasizes hands-on skills required for modern data platforms, including working with Spark, Delta Lake, and automated workflows.

Skills gained:

  • Building data pipelines using SQL and Python
  • Working with Apache Spark and Delta Lake
  • Implementing data governance and security
  • Optimizing performance and workloads

Pro Tip: Practice using notebooks (Python + SQL). Real-world data engineering relies heavily on hands-on coding, not just concepts.

Azure Databricks is a cloud-based platform for large-scale data engineering, analytics, and AI.

It is built on Apache Spark and enables organizations to process massive datasets efficiently. It also supports the lakehouse architecture, combining data lakes and warehouses into a unified platform.

Why it matters:

  • Handles large-scale data processing
  • Supports real-time and batch workloads
  • Integrates with AI and analytics tools
  • Enables modern lakehouse architecture

Pro Tip: Learning Databricks gives you exposure to both data engineering and analytics, increasing your versatility in the job market.

Yes, it prepares you for the Azure Databricks Data Engineer Associate certification.

The course aligns with Microsoft’s certification path and focuses on practical skills such as building pipelines, managing data, and implementing governance.

What it covers for certification:

  • Data ingestion and transformation
  • Pipeline deployment and monitoring
  • Data governance with Unity Catalog
  • Performance optimization

Pro Tip: Focus on understanding real-world scenarios. Certification exams often test practical application, not just theory.

DP-750 focuses on data engineering pipelines, while DP-800 focuses on AI-enabled database applications.

Both courses are complementary but serve different roles in the data ecosystem.

Key differences:

  • DP-750T00:
    • Focus: Data pipelines and lakehouse architecture
    • Tools: Databricks, Spark, Delta Lake
    • Role: Data Engineer
  • DP-800T00:
    • Focus: AI-enabled database solutions
    • Tools: Azure SQL + AI services
    • Role: AI Data Developer

Which should you choose?

  • Choose DP-750 if your goal is to build scalable data platforms
  • Choose DP-800 if your goal is to build AI-powered applications

Pro Tip: If possible, combine both skillsets. Data engineers with AI capabilities are significantly more valuable in the market.

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