DP-750T00: Implement data engineering solutions using Azure Databricks

DP-750 training

Instructor-led Microsoft training for professionals who need practical skills with Implement data engineering solutions using Azure Databricks. Dynamics Edge uses the official Microsoft course as the topic source and rewrites the page into a clear, buyer-focused outline for public classes, private team training, and implementation readiness.

Why choose Dynamics Edge for DP-750T00 training?

Dynamics Edge turns Microsoft course topics into practical instructor-led training for learners who need job skills, certification preparation, course review, and project-ready capability. The class can be delivered as a public course, private team course, government training, or customized workshop.

• Learn from a Microsoft-focused training provider with practical business applications, cloud, security, data, AI, and Power Platform delivery experience.
• Use structured course review, hands-on discussion, and implementation examples instead of only reading a catalog outline.
• Request private team delivery for role-based learning, project onboarding, government teams, migration work, or enterprise adoption.

What will you learn in DP-750T00 training?

This course helps learners understand the official Microsoft topics and apply them through clear outcomes, instructor discussion, practice activities, and review questions.

• Learn Data Engineer and apply it to practical course scenarios.
• Apply Data engineering and apply it to practical course scenarios.
• Review related certifications Microsoft Certified: Azure Databricks Data Engineer Associate and apply it to practical course scenarios.
• Review overview and apply it to practical course scenarios.
• Implement, secure, and maintain scalable lakehouse solutions.

Azure Databricks DP-750 Course Outline

Module 1: Explore Azure Databricks

Students begin by learning the purpose, capabilities, and workspace experience of Azure Databricks. They explore the Databricks workspace UI, understand key workloads, and learn how notebooks, Unity Catalog, clusters, SQL warehouses, and Microsoft Purview fit into the Databricks data engineering lifecycle.

Topics include:

• Azure Databricks workspace navigation.
• Core Azure Databricks workloads.
• Notebooks, SQL, Python, and Markdown.
• Unity Catalog volumes.
• Databricks Assistant / Genie Code.
• Data governance with Unity Catalog and Microsoft Purview.

Lab 01: Explore Azure Databricks
Students explore the Azure Databricks workspace, upload data to a Unity Catalog volume, and use notebook features with Python, SQL magic commands, and Markdown. The lab scenario uses CityMoves Transit, a fictional public transportation authority.

Module 2: Select and Configure Compute in Azure Databricks

Students learn how to choose and configure compute resources for different workloads. This includes all-purpose clusters, job clusters, SQL warehouses, serverless options, autoscaling, libraries, and access permissions.

Topics include:

• Choosing the right compute type.
• Configuring compute performance.
• Autoscaling and cluster policies.
• Installing cluster-scoped and notebook-scoped libraries.
• Managing compute access.
• Using PySpark workloads on compute.

Lab 02: Select and Configure Compute
Students create and configure an all-purpose cluster, install libraries, and use the faker library to generate and analyze synthetic healthcare admission records with PySpark.

Module 3: Create and Organize Objects in Unity Catalog

Students learn how Unity Catalog organizes enterprise data assets. They create catalogs, schemas, managed tables, views, volumes, functions, and governance-friendly naming structures.

Topics include:

• Unity Catalog naming conventions.
• Creating catalogs and schemas.
• Creating tables, views, and volumes.
• Applying DDL operations.
• Using managed tables.
• Creating reusable SQL functions.
• Understanding foreign catalogs.
• Configuring AI/BI Genie instructions.

Lab 03: Create and Organize Objects in Unity Catalog
Students build a complete Unity Catalog namespace for a university data platform. They create a catalog, medallion schemas, managed tables, primary and foreign key constraints, views, volumes, SQL functions, and governance tags.

Learning Path 2: Secure and govern Unity Catalog objects in Azure Databricks

Module 4: Secure Unity Catalog Objects

Students learn how to secure Databricks data assets using Unity Catalog permissions, row filters, column masks, Azure Key Vault secrets, service principals, and managed identities.

Topics include:

• Unity Catalog query lifecycle.
• Access control strategies.
• Fine-grained permissions.
• Row filtering.
• Column masking.
• Azure Key Vault secret access.
• Service principal authentication.
• Managed identity authentication.

Lab 04: Secure Unity Catalog Objects
Students secure a retail data platform by granting fine-grained permissions, applying row filters, masking PII email addresses, and retrieving secrets from Azure Key Vault.

Module 5: Govern Unity Catalog Objects

Students learn how to apply governance practices to Databricks environments. This includes table definitions, tags, policies, data retention, lineage, audit logging, Delta Sharing, and compliance-focused monitoring.

Topics include:

• Creating and preserving table definitions.
• Configuring attribute-based access control with tags and policies.
• Applying data retention policies.
• Using VACUUM.
• Enabling predictive optimization.
• Setting up and managing data lineage.
• Configuring audit logging.
• Designing a secure Delta Sharing strategy.

Lab 05: Govern Unity Catalog Objects
Students apply governance controls to a connected vehicle data platform. They classify PII with tags, configure retention policies, use VACUUM, enable predictive optimization, trace lineage, and analyze audit logs.

Learning Path 3: Prepare and process data with Azure Databricks

Module 6: Design and Implement Data Modeling with Azure Databricks

Students learn how to design lakehouse data models using Delta Lake and Unity Catalog. They compare ingestion patterns, choose table formats, design partitioning and clustering strategies, and implement slowly changing dimensions and temporal history.

Topics include:

• Data ingestion logic and source configuration.
• Choosing data ingestion tools.
• Choosing table formats.
• Delta Lake data modeling.
• Partitioning strategies.
• Slowly changing dimensions.
• SCD Type 2 implementation.
• Temporal history tables.
• Managed vs. unmanaged tables.
• Liquid clustering.
• Change Data Feed.
• Delta Lake time travel.

Lab 06: Design and Implement Data Modeling
Students design a Delta Lake data model for a financial services scenario. They build an SCD Type 2 customer dimension, a transaction fact table with liquid clustering, a compliance audit trail with Change Data Feed, and use time travel to inspect table versions.

Module 7: Ingest Data into Unity Catalog

Students learn multiple ingestion approaches for bringing data into Unity Catalog and Delta Lake, including batch, streaming, COPY INTO, CTAS, Auto Loader, Lakeflow Connect, and Lakeflow Spark Declarative Pipelines.

Topics include:

• Lakeflow Connect.
• Notebook-based ingestion.
• SQL ingestion methods.
• COPY INTO.
• CREATE TABLE AS SELECT.
• Change Data Capture feeds.
• Spark Structured Streaming.
• Auto Loader.
• Lakeflow Spark Declarative Pipelines.

Lab 07: Ingest Data into Unity Catalog
Students load CSV files from Unity Catalog volumes into Delta tables using PySpark DataFrames, SQL COPY INTO, and CTAS. They also configure Auto Loader for continuously arriving files.

Module 8: Cleanse, Transform, and Load Data into Unity Catalog

Students learn how to clean, transform, and load data using SQL and PySpark. They profile data, resolve duplicates and nulls, choose proper data types, transform records with filters and aggregations, join datasets, and load results with merge, insert, and append operations.

Topics include:

• Data profiling.
• Choosing column data types.
• Handling nulls and duplicates.
• Filtering and aggregating data.
• Joining datasets.
• Using set operators.
• Denormalizing data.
• Using PIVOT and UNPIVOT.
• Loading data with MERGE, INSERT, and APPEND.

Lab 08: Cleanse, Transform, and Load Data
Students clean and reshape real estate data. They correct data types, remove duplicate listings, fill missing values, join tables, and use SQL PIVOT and UNPIVOT to restructure market statistics.

Module 9: Implement and Manage Data Quality Constraints with Azure Databricks

Students learn how to validate data and enforce data quality using schema enforcement, type checks, validation rules, schema drift handling, Auto Loader rescued data columns, and Lakeflow pipeline expectations.

Topics include:

• Validation checks.
• Data type checks.
• Schema enforcement.
• Schema drift detection.
• Auto Loader rescued data columns.
• Lakeflow Spark Declarative Pipeline expectations.
• Pipeline monitoring for data quality.

Lab 09: Implement and Manage Data Quality Constraints
Students build a Lakeflow Spark Declarative Pipeline for an insurance claims scenario. They enforce nullability and range checks, validate data types, handle schema drift, and monitor pipeline execution.

Learning Path 4: Deploy and maintain data pipelines and workloads with Azure Databricks

Module 10: Design and Implement Data Pipelines with Azure Databricks

Students learn how to design production data pipelines using notebooks, Lakeflow Pipelines, Lakeflow Jobs, medallion architecture, task dependencies, retry logic, error handling, and branching.

Topics include:

• Designing pipeline order of operations.
• Choosing notebooks vs. Lakeflow Pipelines.
• Designing Lakeflow job logic.
• Implementing error handling.
• Configuring retry policies.
• Using if/else branching.
• Creating notebook-based pipelines.
• Creating Lakeflow Spark Declarative Pipelines.

Lab 10: Design and Implement Data Pipelines
Students build a Bronze, Silver, and Gold medallion pipeline for hotel booking data. They clean source data, produce Gold-layer aggregations, parameterize notebooks, configure task dependencies, and implement error handling.

Module 11: Implement Lakeflow Jobs with Azure Databricks

Students learn how to configure and automate Lakeflow Jobs, including job parameters, triggers, scheduling, alerts, automatic restarts, retries, and file-arrival events.

Topics include:

• Lakeflow job setup.
• Job configuration.
• Parameterized notebooks.
• Scheduled triggers.
• File-arrival triggers.
• Cron scheduling.
• Job alerts and notifications.
• Automatic restarts and retries.

Lab 11: Implement Lakeflow Jobs
Students automate a telecommunications Call Detail Records pipeline. They configure a Lakeflow Job with task dependencies, parameters, scheduled triggers, event-based triggers, notifications, and retry policies.

Module 12: Implement Development Lifecycle Processes in Azure Databricks

Students learn software engineering practices for Databricks development, including Git integration, branching, pull requests, testing strategies, pytest, Databricks CLI, and Declarative Automation Bundles.

Topics include:

• Git version control best practices.
• Branching strategies.
• Pull requests.
• Testing strategies.
• Unit testing with pytest.
• Declarative Automation Bundles.
• Packaging Databricks projects.
• Deploying bundles with the Databricks CLI.

Lab 12: Implement Development Lifecycle Processes
Students implement tests for a data transformation pipeline, then package and deploy the pipeline as a Declarative Automation Bundle using the Databricks CLI.

Module 13: Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks

Students learn how to monitor compute consumption, troubleshoot Lakeflow Jobs, diagnose Spark jobs, use the Spark UI, investigate shuffle and skew issues, optimize joins, and stream logs to Azure Log Analytics.

Topics include:

• Monitoring cluster consumption.
• Troubleshooting Lakeflow Jobs.
• Repairing failed jobs.
• Troubleshooting Spark jobs and notebooks.
• Using the Spark UI.
• Investigating caching, skew, spilling, and shuffle.
• Broadcast joins.
• Adaptive Query Execution.
• Azure Log Analytics integration.

Lab 13: Monitor, Troubleshoot, and Optimize Workloads
Students generate synthetic workloads with intentional skew and shuffle problems, diagnose issues using the Spark UI, and apply fixes using broadcast joins, Adaptive Query Execution, and shuffle reduction.

What hands-on labs and practice activities are included?

Hands-on activities vary by Microsoft course, but Dynamics Edge uses the official course themes to create practical exercises, demonstrations, review tasks, and implementation discussions.

• Guided instructor demonstration based on the main Microsoft course topics.
• Practice activity: configure, build, analyze, secure, automate, or review the primary service, app, or platform covered by the course.
• Enhancement lab: apply the course topics to a real enterprise, government, or team training scenario.
• Enhancement lab: review implementation risks, adoption planning, security considerations, and operational readiness.

How does DP-750T00 training support certification preparation?

This course supports Microsoft certification preparation by connecting course topics to the official study guide, instructor-led review, practice discussion, and job-task outcomes.

Who should attend DP-750T00 training?

This course is for professionals who need Microsoft skills for implementation, administration, development, analytics, security, AI, cloud operations, or business applications work.

• Role-based learners preparing for Microsoft job tasks, project work, or course review, including certification review when applicable.
• Technical consultants, developers, administrators, analysts, architects, security professionals, makers, or functional consultants aligned to the course topic.
• Enterprise, government, and private teams that need consistent Microsoft training for adoption, governance, implementation, support, or modernization.

What are the prerequisites for DP-750T00 training?

Prerequisites vary by Microsoft course level, but learners should understand the basic platform, product, or job role connected to the course. For advanced courses, prior hands-on experience with the related Microsoft technology is recommended.

• Fundamentals courses are appropriate for new learners, business users, and decision makers.
• Associate and expert courses usually require hands-on experience with the product, admin center, developer tools, data platform, security portal, or cloud service.
• Private training can be adjusted for mixed teams with different experience levels.

Can Dynamics Edge deliver private team or government DP-750T00 training?

Yes. Dynamics Edge can deliver this Microsoft course as public training, private team training, government training, onsite training by request, or a customized workshop aligned to your implementation or adoption plan.

• Private classes can emphasize your organization’s cloud environment, business applications, security requirements, data model, governance standards, or project timeline.
• Government and regulated teams can request role-based delivery with security, adoption, and operational readiness discussion.
• Dynamics Edge can combine this course with related Microsoft courses, workshops, or custom implementation training.

Frequently asked questions about DP-750T00 training

Is DP-750T00 an official Microsoft course?

Yes. The course page is based on official Microsoft Learn course topics and then rewritten into a Dynamics Edge training outline for readability, SEO, and buyer clarity.

Can this course be delivered privately?

Yes. Dynamics Edge can deliver private training for teams that need customized examples, project alignment, government delivery, or role-based Microsoft training.

Does this course help with certification?

If the course maps to a Microsoft exam or certification, Dynamics Edge can include certification review, study guide alignment, practice discussion, and exam preparation guidance.

Course Review

Before attending class, review the course modules and identify the topics most important to your job role, project, or certification plan. Bring questions about implementation, configuration, development, analytics, security, AI, governance, or operational readiness so the instructor can connect the course to real work.

Certification Exam Review

After class, use the course outline, certification page, study guide, and instructor review points to plan focused practice. Spend additional time on the highest-weighted exam areas if an official Microsoft exam skills map is available.

How do I register for DP-750T00 training?

Register for Dynamics Edge instructor-led Microsoft training to build practical skills for Implement data engineering solutions using Azure Databricks, team readiness, implementation support, and certification preparation.