Course Price: Original price was: $2,000.00.$1,500.00Current price is: $1,500.00.
Course Price
Original price was: $2,000.00.$1,500.00Current price is: $1,500.00.
25% OFF. Expires in
Azure Databricks Training for Data Engineer (DP-750)
The Azure Databricks Training for Data Engineer (DP-750) instructor-led training is designed for professionals who want to build, optimize, and manage scalable data engineering solutions using Azure Databricks and Apache Spark. This Azure Databricks Training provides hands-on, real-world experience with designing data ingestion and transformation workflows, managing Delta Lake tables, and implementing data engineering best practices on Azure. Students learn how to process batch and streaming data, apply data quality and governance controls, and optimize Spark workloads for performance and cost efficiency.This course is intended for Data engineers responsible for building and maintaining data pipelines and lakehouse architectures on Azure, Analytics engineers who work with data transformation and data modeling in cloud environments, Professionals with experience in data engineering who are preparing for the DP-750 certification exam, Developers seeking to apply Azure Databricks to production data engineering challenges.
The Azure Databricks Training for Data Engineer (DP-750) instructor-led training is aligned with the Microsoft DP-750 certification exam, this course focuses on practical implementation skills. Participants work with Azure Databricks Workspaces, notebooks, and jobs to design real-life data engineering solutions that support analytics, BI, and machine learning workloads. By the end of this course, participants will be able to:
- Design and implement scalable data engineering solutions using Azure Databricks and Apache Spark
- Build batch and streaming data pipelines using Delta Lake and Databricks workflows
- Optimize Spark jobs for performance, reliability, and cost efficiency
- Implement data governance, security, and monitoring in Azure Databricks environments
- Prepare confidently for the DP‑750: Azure Databricks certification exam
Who should attend the DP-750: Implementing Data Engineering Solutions Using Azure Databricks course ?
For Professionals
This training is ideal for professionals who design, build, or operate large-scale data pipelines and analytics platforms using Azure and Apache Spark. Recommended job roles including Data Engineers, Azure Data Engineers, Big Data Engineers, Analytics Engineers, Cloud Engineers, Data Platform Developers, Professionals preparing for the DP-750 certification.
For Businesses
Organizations should nominate professionals who are responsible for building, scaling, and optimizing data platforms that support analytics, reporting, and AI workloads. Recommended roles to nominate Enterprise Data Engineering Teams, Cloud & Platform Engineering Teams, Analytics and BI Engineering Teams, Data Platform Modernization Teams, Teams migrating from on‑prem or legacy data platforms to Azure.
Prerequisites for the "DP-750: Implementing Data Engineering Solutions Using Azure Databricks" Course
Students should have the following knowledge and experience before attending this course:
- Fundamental knowledge of data analytics and data warehousing concepts
- Basic understanding of cloud storage and Azure resource management
- Familiarity with SQL for querying and managing data
- Basic understanding of Python programming (used in notebooks and PySpark)
- Understanding of Git and version control fundamentals
- Knowledge of Microsoft Entra ID and Azure security basics
Key Features of Flexmind DP-750: Implementing Data Engineering Solutions Using Azure Databricks Training
This training is delivered by Flexmind through flexible online and in‑person formats and is fully aligned with the latest certification exam requirements. Key features of the training include:
Course Duration
The course has a total duration of 32 hours and is completed over 4 days.
Instructor-Led Training
Delivered by a Senior Microsoft Certified Trainer with real-world, enterprise-scale experience as a data engineer and architect in Azure Databricks implementations.
Microsoft Official curriculum
Delivered by Flexmind using official Microsoft curriculum, this program blends study material, hands-on labs, and applied workshops with instructor-led guidance throughout.
Cloud Lab Access
The course will be covered using cloud lab access.
Course Completion Certificate
Course completion includes certification, formally validating the skills gained and reinforcing professional credibility.
Course Outline - Azure Databricks Training for Data Engineer (DP-750)
Module 1: Explore Azure Databricks
- Get Started With Azure Databricks
- Identify Azure Databricks Workloads
- Understand Key Concepts
- Data Governance Using Unity Catalog and Microsoft Purview
- Lab 01: Explore Azure Databricks
Module 2: Select and Configure Compute in Azure Databricks
- Choose an appropriate compute type
- Configure compute performance
- Configure compute features
- Install libraries for compute
- Configure compute access
- Lab 02: Select and Configure Compute in Azure Databricks
Module 3: Create and organize objects in Unity Catalog
- Apply naming conventions
- Create catalog
- Create schema
- Create tables and views
- Create volumes
- Implement DDL operations
- Implement foreign catalog
- Configure AI/BI Genie instructions
- Lab 03: Create and Organize Objects in Unity Catalog
Module 4: Secure Unity Catalog objects
- Understand query lifecycle
- Implement access control strategies
- Understand fine-grained access control
- Implement row filtering and column masking
- Access Azure Key Vault secrets
- Authenticate data access with service principals
- Authenticate resource access with managed identities
- Lab 04: Secure Unity Catalog Objects
Module 5: Govern Unity Catalog objects
- Create and preserve table definitions
- Configure ABAC with tags and policies
- Apply data retention policies
- Set up and manage data lineage
- Configure audit logging
- Design secure Delta Sharing strategy
- Lab 05: Govern Unity Catalog Objects
Module 6: Design and implement data modeling with Azure Databricks
- Design ingestion logic and data source configuration
- Choose a data ingestion tool
- Choose a data table format
- Design and implement a data partitioning scheme
- Choose a slowly changing dimension (SCD) type
- Implement a slowly changing dimension (SCD) type 2
- Design and implement a temporal (history) table to record changes over time
- Choose granularity on a column or table based on requirements
- Choose managed vs unmanaged tables
- Design and implement a clustering strategy
- Lab 06: Design and implement data modeling with Azure Databricks
Module 7: Ingest data into Unity Catalog
- Ingest data with Lakeflow Connect
- Ingest data with notebooks
- Ingest data with SQL methods
- Ingest data with CDC feed
- Ingest data with Spark Structured Streaming
- Ingest data with Auto Loader
- Ingest data with Lakeflow Spark Declarative Pipelines
- Lab 07: Ingest Data into Unity Catalog
Module 8: Cleanse, transform, and load data into Unity Catalog
- Ingest data with Lakeflow Connect
- Profile data
- Choose column data types
- Resolve duplicates and nulls
- Transform data with filters and aggregations
- Transform data with joins and set operators
- Transform data with denormalization and pivots
- Load data with merge, insert, and append
- Load data with merge, insert, and append
- Lab 08: Cleanse, transform, and load data into Unity Catalog
Module 9: Implement and manage data quality constraints with Azure Databricks
- Implement validation checks
- Implement data type checks
- Detect and manage schema drift
- Manage data quality with pipeline expectations
- Lab 09: Implement and Manage Data Quality Constraints in Unity Catalog
Module 10: Design and implement data pipelines with Azure Databricks
- Design order of operations for a pipeline
- Choose notebook vs Lakeflow Pipelines
- Design Lakeflow job logic
- Design error handling in pipelines and jobs
- Create pipeline with notebook
- Create pipeline with Lakeflow Spark Declarative Pipelines
- Lab 10: Design and implement data pipelines with Azure Databricks
Module 11: Implement Lakeflow Jobs with Azure Databricks
- Create job setup and configuration
- Configure job triggers
- Schedule a job
- Configure job alerts
- Configure automatic restarts
- Lab 11: Implement Lakeflow Jobs with Azure Databricks
Module 12: Implement development lifecycle processes in Azure Databricks
- Apply Git version control best practices
- Manage branching and pull requests
- Implement testing strategy
- Configure and package DABs
- Deploy bundle with Databricks CLI
- Lab 12: Implement Development Lifecycle Processes in Azure Databricks
Module 13: Monitor, troubleshoot and optimize workloads in Azure Databricks
- Monitor and manage cluster consumption
- Troubleshoot and repair Lakeflow Jobs
- Troubleshoot Spark jobs and notebooks
- Implement log streaming with Azure Log Analytics
- Lab 13: Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks
Class Schedule
Instructor‑Led Training
- 32 Hours of Instructor‑Led Training
- One‑to‑one doubt‑resolution sessions
- Microsoft Official Lab Access
Learning Objectives - Azure Databricks Training for Data Engineer (DP-750)
After completing the DP-750 course, learners will be able to:
- Design and implement scalable data engineering solutions using Azure Databricks, Apache Spark, and lakehouse architecture patterns.
- Build and manage batch and streaming data pipelines using Delta Lake to ensure reliability, performance, and data consistency.
- Optimize Spark workloads for performance and cost efficiency through effective partitioning, caching, and cluster configuration.
- Implement secure, governed data platforms by applying access controls, monitoring, and data quality best practices in Azure Databricks.
- Prepare for the DP-750 certification exam by gaining hands‑on experience aligned with Microsoft‑recommended data engineering scenarios and tools.
About DP-750 Certification Exam
To help you understand the assessment better, here are a few important details about the exam.
| Exam Name | DP-750: Implementing Data Engineering Solutions Using Azure Databricks |
|---|---|
| Who should Apply | Data Engineer |
| Duration of Exam | Around 120 Minutes |
| Fees | Rs. 4,865 (India), $165 USD (United States) |
| Level of Difficulty | Intermediate |
| Type of Credential | Microsoft Certification |
| Languages | English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia) |
| Exam Retake | Exam retake allowed after 24 hours |
| Quality Check during Assessment | The online exam is proctored |
The table below represents the weightage of each study area in the exam. Areas with higher percentages are expected to have more questions.
| Study Area | Percentage |
|---|---|
| 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% |
