DP-750 Course – Data Engineering with Azure Databricks on Microsoft Azure
The course DP-750: Implement data engineering solutions using Azure Databricks is an official Microsoft training course focused on the design and implementation of data engineering solutions on Microsoft Azure using Azure Databricks. This course covers how to process, transform, and manage large volumes of data in cloud environments, applying modern data architectures.
During the training, participants learn to build data pipelines, automate ingestion and transformation processes, and work with structured and unstructured data in business scenarios. It is aimed at technical profiles such as data engineers, developers, cloud architects, and analytics specialists who manage data platforms in organizations.
The practical value of the DP-750 course lies in its business-focused approach, allowing the development of scalable solutions that optimize data processing, improve information quality, and facilitate advanced analytics. As an official Microsoft course, it guarantees up-to-date content aligned with industry best practices. Furthermore, this training is eligible for FUNDAE subsidies, facilitating its integration into corporate training plans.
DP-750 Course Overview
The course DP-750: Implement data engineering solutions using Azure Databricks is an official Microsoft training course focused on the design and development of data engineering solutions in cloud environments using Microsoft Azure and Azure Databricks. This course allows professionals to work with large volumes of data, applying modern technologies for cloud data processing and analysis.
Throughout the training, students will learn to configure and manage data clusters, develop processing pipelines, and transform datasets using collaborative environments such as Databricks. The course covers real-world scenarios where data ingestion, transformation, and storage are key to analytics systems, artificial intelligence, and business reporting.
This training is especially aimed at technical profiles such as data engineers, developers, and cloud architects who work on projects where efficient data management is critical. Its practical approach allows the implementation of scalable solutions that optimize data flows and improve information quality in business environments.
Virtual course with certification exam included as a gift. Don't miss this opportunity! The exam is valued at €126 + VAT and is included at no additional cost.
Promotion valid until December 31, 2026. One-attempt exam available only in Virtual - Distance Learning mode.
What is the DP-750 Microsoft certification used for in the professional environment?
The DP-750 certification, focused on data engineering with Azure Databricks within the Microsoft Azure ecosystem, allows professionals to demonstrate their ability to design and manage large-scale data processing solutions. In the job market, this certification validates key skills in data processing, transformation, and preparation for advanced analytics and artificial intelligence projects.
It is aimed at technical profiles such as data engineers, developers, cloud architects, and data consultants who work on real projects where it is necessary to build robust data pipelines, automate ingestion processes, and prepare information for analytics and reporting systems. In companies, these skills are directly applied to modern data platforms, cloud integrations, and Big Data solutions.
At a professional level, the DP-750 course facilitates specialization in data engineering on Azure, improving employability and allowing access to key roles in digital transformation projects. It provides the necessary capabilities to manage the complete data lifecycle, optimize processes, and contribute to data-driven decision-making in business environments.
Professional Applications of Azure Databricks
Azure Databricks, integrated into the Microsoft Azure ecosystem, is an analytics and data processing platform designed to work with large volumes of information in business environments. In the context of the DP-750 course, this technology is used to build data engineering solutions that allow the automation of data processing and the preparation of information for subsequent analysis.
In practice, Azure Databricks is applied in multiple business scenarios: automation of data ingestion and transformation processes, development of data pipelines for analytics applications, analysis of large datasets for business intelligence, and support for reporting and decision-making systems. For example, it allows the processing of sales data, optimization of logistics operations, or integration of information from different corporate systems into a single data platform.
The DP-750 course provides an applied perspective that allows the implementation of these solutions in real projects, helping organizations improve data management, reduce processing times, and facilitate the development of scalable solutions aligned with business needs.
What is included in the official Microsoft courses and certifications at Nanfor
The training includes Microsoft Learn material, presentations by expert tutors, authorized labs, specialized tutoring, personalized sessions with an expert tutor, certification preparation, and a certificate of completion, combining institutional content with Nanfor's expert support.
Learn all the components
Advantages of DP-750 Training
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Specialization in data engineering with Azure Databricks: Development of modern large-scale data processing and transformation solutions.
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Building scalable data pipelines: Automation of data ingestion, transformation, and deployment in cloud environments.
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Complete integration with the Azure ecosystem: Connection with services such as Azure Storage, Data Factory, or Power BI for end‑to‑end solutions.
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Optimization of data processing and analysis: Improved performance and reduced times in Big Data and analytics projects.
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High demand for Data Engineer profiles: Training aligned with one of the most sought-after roles in data and digital transformation projects.
Prerequisites
To make the most of the DP‑750 course, it is recommended:
- Basic knowledge of data analysis and data structures
- Experience with SQL for querying and transforming data
- Knowledge of Python applied to data engineering
- Familiarity with Microsoft Azure and cloud environments
- Experience or knowledge of data pipelines, ETL, and data modeling
Preparation for the Microsoft DP-750 Certification
The Nanfor DP‑750 training prepares you for the official Microsoft Certified: Azure Databricks Data Engineer Associate certification, validating the skills necessary to design, implement, and maintain data engineering solutions in business environments using Azure Databricks within the Microsoft Azure ecosystem.
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Course duration:
100 hours
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Classroom access:
3 months
General course information
Who is the Microsoft DP-750 course aimed at?
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Data Engineers working with Azure Databricks
- Developers and data solution architects in the cloud
- Analytics and Big Data professionals in Azure environments
- Specialists in data pipelines, ETL, and Lakehouse architectures
- Profiles with experience in SQL, Python, and data processing
DP-750 course objectives and what you will learn
Upon completion of the DP‑750 course, the participant will be able to:
- Design and implement data engineering solutions with Azure Databricks
- Create and manage scalable data pipelines in cloud environments
- Ingest, transform, and model data using SQL, Python, and Apache Spark
- Apply modern architectures such as Lakehouse for business data management
- Implement data security, governance, and quality practices with Unity Catalog
- Monitor, optimize, and maintain data workloads in production
- Integrate Azure Databricks with Azure ecosystem services in end‑to‑end solutions
Microsoft Learn DP-750 collection elements
- Configure and manage an Azure Databricks environment
- Protect and govern objects in Unity Catalog in Azure Databricks
- Prepare and process data with Azure Databricks
- Deploy and maintain data pipelines and workloads in Azure Databricks
DP-750 Course Content – Official Program
Unit 1: Explore Azure Databricks
- Getting started with Azure Databricks
- Identify Azure Databricks workloads
- Understanding key concepts
- Data governance through Unity Catalog and Microsoft Purview
- Lab 1: Explore Azure Databricks
Unit 2: Select and configure the process in Azure Databricks
- Choose a suitable process type
- Configure process performance
- Configure process features
- Install libraries for the process
- Configure process access
- Lab 2: Select and configure the process in Azure Databricks
Unit 3: Create and organize objects in Unity Catalog
- Apply naming conventions
- Create a catalog
- Create a schema
- Create tables and views
- Create volumes
- Implement DDL operations
- Implement external catalog
- Configure AI/BI Genie instructions
- Lab 3: Create and organize objects in Unity Catalog)
Unit 4: Protect Unity Catalog objects
- Describe the query lifecycle
- Implement access control strategies
- Understand specific access control
- Implement row filtering and column masking
- Access secrets from Azure Key Vault
- Authenticate data access with service principals
- Authenticate resource access using managed identities
- Lab 4: Protect Unity Catalog objects
Unit 5: Control Unity Catalog objects
- Create and maintain table definitions
- Configure ABAC with tags and policies
- Apply data retention policies
- Configure and manage data lineage
- Configure audit logging
- Design a secure Delta Sharing strategy
- Lab 5: Control Unity Catalog objects
Unit 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 partitioning scheme
- Choose a slowly changing dimension (SCD) type
- Implement a Type 2 SCD dimension
- Design a temporary table for history
- Choose the appropriate granularity
- Choose managed or unmanaged tables
- Design a clustering strategy
- Lab 6: Design and implement a data model in Unity Catalog
Unit 7: Ingest data into Unity Catalog
- Ingest data with Lakeflow Connect
- Data ingestion with notebooks
- Data ingestion with SQL methods
- Data ingestion with CDC source
- Ingest data with Spark Structured Streaming
- Ingest data with Autoloader
- Ingest data with Lakeflow declarative pipelines
- Lab 7: Ingest data into Unity Catalog
Unit 8: Clean, transform, and load data into Unity Catalog
- Generate data profiles
- Choose column data types
- Resolve duplicates and nulls
- Transform data with filters and aggregations
- Transform data with joins
- Transform data with denormalization and pivots
- Load data with combination, insertion, and appending
- Lab 8: Clean and transform data in Unity Catalog
Unit 9: Implement and manage data quality constraints
- Implement validation checks
- Implement data type checks
- Detect and manage schema drift
- Manage data quality with expectations
- Lab 9: Implement and manage data quality constraints
Unit 10: Design and implement data pipelines
- Design the order of operations
- Choose Notebook or Lakeflow Pipelines
- Design Lakeflow workflow logic
- Design error handling
- Create pipeline with notebooks
- Create pipeline with declarative pipelines
- Lab 10: Design and implement data pipelines
Unit 11: Implement Lakeflow jobs
- Create job configuration and installation
- Configure triggers
- Schedule a job
- Configure alerts
- Configure automatic restarts
- Lab 11: Implement Lakeflow jobs
Unit 12: Implement development lifecycle processes
- Apply version control best practices
- Manage branches and pull requests
- Implement testing strategy
- Configure and package DAB
- Implement deployment with Databricks CLI
- Lab 12: Implement development lifecycle processes
Unit 13: Monitor, troubleshoot, and optimize workloads
- Monitor and consume clusters
- Troubleshoot Lakeflow jobs
- Spark troubleshooting
- Implement log streaming
- Lab 13: Monitor and optimize workloads
Unit 14: Course conclusion
- Course summary and wrap-up
- Next steps in learning
Our differentiator: Hands-on labs
| Nanfor labs |
Technical skills developed |
Practical learning outcome |
| Azure Databricks environment preparation |
Workspace creation, initial cloud environment configuration |
The student deploys a complete data engineering environment |
| Exploring Azure Databricks |
Use of notebooks (Python, SQL, Markdown) and collaborative environment |
The student works as a data engineer in a real development environment |
| Cluster and compute configuration |
Creation and management of clusters, libraries, and resources |
The student optimizes distributed processing infrastructure |
| Creating structures in Unity Catalog |
Definition of catalogs, schemas, tables, and views |
The student organizes data with enterprise governance |
| Data security implementation |
Row- and column-level access control, secrets management |
The student protects sensitive data while meeting business requirements |
| Data governance in Lakehouse |
Use of Unity Catalog for data and metadata control |
The student applies data governance in modern platforms |
| Data modeling in Medallion architecture |
Design of Bronze, Silver, and Gold layers |
The student structures data for advanced analytics |
| Data ingestion in Databricks |
Data loading from multiple sources |
The student connects real business data sources |
| Data transformation (ETL/ELT) |
Cleaning, transformation, and loading with PySpark and SQL |
The student builds robust data pipelines |
| Data quality management |
Implementation of constraints and validations |
The student ensures data integrity and consistency |
| Data pipeline design |
Creation of scalable processing pipelines |
The student automates data transformation |
| Automation with jobs (Lakeflow / workflows) |
Programming and automatic execution of tasks |
The student implements automated production processes |
| Integration with Azure services |
Integration with Data Factory, monitoring, and security |
The student connects Databricks with the Azure ecosystem |
| Lifecycle management (DevOps) |
Versioning, deployment, and development control |
The student applies data development best practices |
| Pipeline monitoring |
Use of metrics, logs, and tracking tools |
The student detects errors and maintains active solutions |
| Performance optimization |
Query adjustment, Spark tuning, and resource optimization |
The student improves costs and processing efficiency |
| End-to-end solution development |
Complete integration of ingestion, transformation, and deployment |
The student implements a complete, production-ready data solution |
Course language
- Course: English / Spanish
- Labs: English / Spanish
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Nanfor, Microsoft's official ICT training center
Nanfor is a custom ICT training center specializing in technology training for professionals and businesses, and it is officially accredited by Microsoft as:
- Microsoft Solutions Partner – Training Services
- Microsoft Cloud Partner
These accreditations certify that Nanfor meets Microsoft's standards for delivering technical courses, using Microsoft content and Microsoft Certified Trainers (MCTs), ensuring quality, continuous updates, and alignment with certifications.
Frequently asked questions about the DP-750 course
What is the Microsoft Azure Databricks DP-750 course?
The DP-750 course is official Microsoft training focused on implementing data engineering solutions using Azure Databricks within the Microsoft Azure ecosystem.
What is the Microsoft DP-750 certification in Azure Databricks used for?
The DP-750 certification validates the ability to design, build, and manage scalable data pipelines in cloud environments, applying data engineering in Azure Databricks for business projects.
Is the Microsoft DP-750 course on data engineering in Azure official?
Yes. The DP-750 course is official Microsoft training based on Microsoft Learn and aligned with best practices in data engineering and cloud solutions.
What is the difference between the Microsoft Azure Databricks DP-750 course and other Microsoft certifications?
The DP-750 course focuses specifically on data engineering with Azure Databricks and Lakehouse architectures, while other Azure certifications address storage, analytics, or development more generally.
Is the Microsoft Azure Databricks DP-750 course eligible for FUNDAE funding?
Yes. The DP-750 course is eligible for FUNDAE funding, subject to the conditions and requirements established by the company.
What is the duration and access provided for the Microsoft Azure Databricks DP-750 course?
The DP-750 course has an approximate duration of 100 hours and offers access to the virtual classroom for 3 months, allowing you to progress flexibly.
Is the Microsoft Azure Databricks DP-750 course included in Nanfor's LaaS?
Yes. The DP-750 course is part of the LaaS Cert service, facilitating access to this training along with other official Microsoft courses.
What technical level is required for the Microsoft DP-750 course in Azure Databricks?
The DP-750 course requires an intermediate level and is aimed at professionals with knowledge of SQL, Python, Azure, and data engineering concepts.
What career opportunities does the Microsoft Azure Databricks DP-750 course offer?
The DP-750 course provides access to roles such as data engineer, data architect, data solutions developer, or Big Data specialist in Azure-based business environments.
