Implement Data Engineering Solutions Using Azure Databricks DP-750

Module 1Explore Azure Databricks

• Get started with Azure Databricks
• Identify Azure Databricks workloads
• Understand key concepts
• Data governance using Unity Catalog and Microsoft Purview
• Module assessment

Module 2Understand Azure Databricks architecture

• Understand Azure Databricks architecture
• Understand Unity Catalog managed storage
• Understand external storage
• Understand default storage
• Module assessment

Module 3Understand Azure Databricks Integrations

• Understand integration with Microsoft Fabric
• Understand integration with Power BI
• Understand integration with VS Code
• Understand integration with Power Platform
• Understand integration with Copilot Studio
• Understand integration with Microsoft Purview
• Understand integration with Microsoft Foundry
• Module assessment

Module 4Select and Configure Compute in Azure Databricks

• Choose an appropriate compute type
• Configure compute performance
• Configure compute features
• Install libraries for compute
• Configure compute access

Module 5Create 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

Module 6Secure 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
• Module assessment

Module 7Govern 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
• Module assessment

Module 8Design 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 external tables
• Design and implement a clustering strategy

Module 9Ingest 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
• Module assessment

Module 10Cleanse, transform, and load data into Unity Catalog

• 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
• Module assessment

Module 11Implement 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
• Module assessment

Module 12Design 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

Module 13Implement Lakeflow Jobs with Azure Databricks

• Create job setup and configuration
• Configure job triggers
• Schedule a job
• Configure job alerts
• Configure automatic restarts
• Module assessment

Module 14Implement development lifecycle processes in Azure Databricks

• Apply Git version control best practices
• Manage branching and pull requests
• Implement testing strategy
• Configure and package Declarative Automation Bundles
• Deploy bundle with Databricks CLI
• Module assessment

Module 15Monitor, troubleshoot and optimize workloads in Azure Databricks

• Monitor and manage cluster consumption
• Troubleshoot and repair Lakeflow Jobs
• Troubleshoot Spark jobs and notebooks
• Investigate caching, skewing, spilling, shuffle
• Implement log streaming with Azure Log Analytics