DP-750 Course – Data Engineering with Azure Databricks on Microsoft Azure
The DP-750: Implement data engineering solutions using Azure Databricks course is an official Microsoft training focused on designing and implementing data engineering solutions on Microsoft Azure using Azure Databricks. This course addresses 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 applied business focus, enabling the development of scalable solutions that optimize data processing, improve information quality, and facilitate advanced analysis. As an official Microsoft course, it guarantees up-to-date content aligned with industry best practices. Furthermore, this training is eligible for FUNDAE funding, facilitating its integration into corporate training plans.
DP-750 Course Overview
The DP-750: Implement data engineering solutions using Azure Databricks course is an official Microsoft training focused on designing and developing data engineering solutions in cloud environments using Microsoft Azure and Azure Databricks. This course enables professionals to work with large volumes of data, applying modern technologies for data processing and analysis in the cloud.
Throughout the training, students will learn to configure and manage data clusters, develop processing pipelines, and transform datasets using collaborative environments like Databricks. The course addresses real-world scenarios where data ingestion, transformation, and storage are key for analysis 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 for the implementation of scalable solutions that optimize data flows and improve information quality in business environments.
Virtual course with included certification exam 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, 2025. One-time exam available only in Virtual - E-learning mode.
What is the purpose of the Microsoft DP-750 certification in a 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 handling, 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 analytical and reporting systems. In business, these skills are directly applied in modern data platforms, cloud integrations, and Big Data solutions.
On 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 a data analytics and processing platform designed to work with large volumes of information in enterprise environments. In the context of the DP-750 course, this technology is used to build data engineering solutions that automate data processing and prepare information for subsequent analysis.
In practice, Azure Databricks is applied in multiple business scenarios: automating data ingestion and transformation processes, developing data pipelines for analytical applications, analyzing large datasets for business intelligence, and supporting reporting and decision-making systems. For example, it allows processing sales data, optimizing logistical operations, or integrating information from different corporate systems into a single data platform.
The DP-750 course provides an applied vision that allows implementing 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 official Microsoft courses and certifications at Nanfor
The training includes Microsoft Learn materials, expert tutor presentations, authorized labs, specialized tutoring, personalized sessions with an expert tutor, certification preparation, and a certificate of completion, combining institutional content with expert support from Nanfor.
Learn about 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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Full integration with the Azure ecosystem: Connection with services like 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 get the most out of the DP‑750 course, it is recommended:
- Basic knowledge of data analysis and data structures
- Experience with SQL for data querying and transformation
- Knowledge of Python applied to data engineering
- Familiarity with Microsoft Azure and cloud environments
- Experience or notions of data pipelines, ETL, and data modeling
Preparation for Microsoft DP-750 Certification
Nanfor's DP‑750 training prepares for the official Microsoft Certified: Azure Databricks Data Engineer Associate certification, validating the necessary competencies to design, implement, and maintain data engineering solutions in enterprise environments using Azure Databricks within the Microsoft Azure ecosystem.
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Course duration:
100 hours
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Access to the classroom:
3 months
General Course Information
Who is the Microsoft DP-750 course for?
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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, participants 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 like Lakehouse for enterprise data management
- Implement data security, governance, and quality practices with Unity Catalog
- Monitor, optimize, and maintain production data workloads
- Integrate Azure Databricks with Azure ecosystem services for end-to-end solutions
Elements of the DP-750 Microsoft Learn collection
- Set up and administer an Azure Databricks environment
- Secure and govern objects in Unity Catalog on 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 compute in Azure Databricks
- Choose an appropriate compute type
- Configure compute performance
- Configure compute features
- Install libraries for compute
- Configure compute access
- Lab 2: Select and configure compute 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 foreign catalog
- Configure AI/BI Genie statements
- Lab 3: Create and organize objects in Unity Catalog
Unit 4: Secure Unity Catalog objects
- Understanding the query lifecycle
- Implement access control strategies
- Understand specific access control
- Implement row filtering and column masking
- Access Azure Key Vault secrets
- Authenticate data access with service principals
- Authenticate resource access using managed identities
- Lab 4: Secure Unity Catalog objects
Unit 5: Govern 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: Govern 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 an SCD type 2 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
- Ingest data with notebooks
- Ingest data with SQL methods
- Ingest data with CDC source
- Ingest data with Spark structured streaming
- Ingest data with Autoloader
- Ingest data with declarative Lakeflow pipelines
- Lab 7: Ingest data into Unity Catalog
Unit 8: Clean, transform, and load data into Unity Catalog
- Data profiling
- 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 merge, insert, and append
- 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 job 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
- Cluster Monitoring and Consumption
- Lakeflow Job Troubleshooting
- 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 Differentiating Factor: Hands-on Labs
| Nanfor Labs |
Technical Skills Developed |
Practical Learning Outcome |
| Azure Databricks Environment Setup |
Workspace creation, initial cloud environment configuration |
Students deploy a complete data engineering environment |
| Exploring Azure Databricks |
Using notebooks (Python, SQL, Markdown) and collaborative environment |
Students work as data engineers in a real development environment |
| Configuring Clusters and Compute |
Creating and managing clusters, libraries, and resources |
Students optimize distributed processing infrastructure |
| Creating Structures in Unity Catalog |
Defining catalogs, schemas, tables, and views |
Students organize data with enterprise governance |
| Implementing Data Security |
Row and column-level access control, secret management |
Students protect sensitive data meeting business requirements |
| Data Governance in Lakehouse |
Using Unity Catalog for data and metadata control |
Students apply data governance in modern platforms |
| Data Modeling in Medallion Architecture |
Designing Bronze, Silver, and Gold layers |
Students structure data for advanced analytics |
| Data Ingestion in Databricks |
Loading data from multiple sources |
Students connect real business data sources |
| Data Transformation (ETL/ELT) |
Cleaning, transforming, and loading with PySpark and SQL |
Students build robust data pipelines |
| Data Quality Management |
Implementing constraints and validations |
Students ensure data integrity and consistency |
| Data Pipeline Design |
Creating scalable processing pipelines |
Students automate data transformation |
| Automation with Jobs (Lakeflow / workflows) |
Scheduling and automatic execution of tasks |
Students implement automated production processes |
| Integration with Azure Services |
Integration with Data Factory, monitoring, and security |
Students connect Databricks with the Azure ecosystem |
| Lifecycle Management (DevOps) |
Versioning, deployment, and development control |
Students apply data development best practices |
| Pipeline Monitoring |
Using metrics, logs, and monitoring tools |
Students detect errors and maintain active solutions |
| Performance Optimization |
Query tuning, Spark tuning, and resource management |
Students improve processing costs and efficiency |
| End-to-end Solution Development |
Complete integration of ingestion, transformation, and deployment |
Students implement a complete, production-ready data solution |
Course Language
- Course: English / Spanish
- Labs: English / Spanish
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Nanfor, official Microsoft IT training center
Nanfor is a custom IT training center, specialized in technological training for professionals and companies, and is officially accredited by Microsoft as:
- Microsoft Solutions Partner – Training Services
- Microsoft Cloud Partner
These accreditations certify that Nanfor complies with Microsoft's standards for delivering technical courses, using Microsoft content and Microsoft Certified Trainers (MCTs), guaranteeing 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 an official Microsoft training focused on implementing data engineering solutions using Azure Databricks within the Microsoft Azure ecosystem.
What is the purpose of the Microsoft DP-750 certification in Azure Databricks?
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 enterprise projects.
Is the Microsoft DP-750 data engineering on Azure course official?
Yes. The DP-750 course is an 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 specifically focuses 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 for the Microsoft Azure Databricks DP-750 course?
The DP-750 course has an approximate duration of 100 hours and offers virtual classroom access for 3 months, allowing flexible progress.
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 training courses.
What technical level is required for the Microsoft DP-750 course in Azure Databricks?
The DP-750 course requires an intermediate level, 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 allows access to roles such as data engineer, data architect, data solutions developer, or Big Data specialist in enterprise environments based on Azure.
