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Microsoft Data & Power BIDP-750T00: Implement data engineering solutions using Azure Databricks
Engineer data solutions on Azure Databricks.
4 daysIntermediate15 modulesDP-750T00
About this course
Engineer data solutions on Azure Databricks. Over four days you will understand the architecture and integrations, configure compute, organise, secure and govern data in Unity Catalog, ingest and cleanse data with quality checks, build pipelines and Lakeflow Jobs, and apply Git-based development practices.
Curriculum
15 modules
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Data governance using Unity Catalog and Microsoft Purview
- Module assessment
- Understand Azure Databricks architecture
- Understand Unity Catalog managed storage
- Understand external storage
- Understand default storage
- Module assessment
- 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
- Choose an appropriate compute type
- Configure compute performance
- Configure compute features
- Install libraries for compute
- Configure compute access
- Module assessment
- Apply naming conventions
- Create catalog
- Create schema
- Create tables and views
- Create volumes
- Implement DDL operations
- Implement foreign catalog
- Configure AI/BI Genie instructions
- 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
- 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
- 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
- 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
- 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
- Implement validation checks
- Implement data type checks
- Detect and manage schema drift
- Manage data quality with pipeline expectations
- Module assessment
- 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 assessment
- Create job setup and configuration
- Configure job triggers
- Schedule a job
- Configure job alerts
- Configure automatic restarts
- Module assessment
- 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
- 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
- Module assessment
Who it's for
Data engineers with cloud and analytics basics.
Before you start
No formal prerequisites. Bring curiosity and a willingness to practise.
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