The Azure Data Fundamentals Training (DP-900) provides a solid foundation in data concepts and Azure services. Below are the topics covered in this course.
Topics
Skill: Describe Core Data Concepts
Part 1: Representing Data
Session 1: Structured Data
- Describe features of structured data
- Identify common examples of structured data usage
Session 2: Semi-Structured and Unstructured Data
- Describe features of semi-structured data
- Describe features of unstructured data
Part 2: Data Storage Options
Session 3: Data File Formats and Databases
- Describe common formats for data files
- Identify types of databases
Session 4: Common Data Workloads
- Describe features of transactional workloads
- Describe features of analytical workloads
Part 3: Roles and Responsibilities for Data Workloads
Session 5: Key Responsibilities
- Describe the responsibilities of database administrators
- Describe the responsibilities of data engineers
- Describe the responsibilities of data analysts
Skill: Identify Considerations for Relational Data on Azure
Part 1: Relational Concepts
Session 6: Features and Structures
- Identify features of relational data
- Describe normalization and its purposes
Session 7: SQL and Database Objects
- Identify common SQL statements
- Identify common database objects
Part 2: Relational Azure Data Services
Session 8: Azure SQL Family
- Describe Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines
Session 9: Open-Source Databases
- Identify Azure database services for open-source database systems
Skill: Describe Considerations for Working with Non-Relational Data on Azure
Part 1: Azure Storage Capabilities
Session 10: Blob, File, and Table Storage
- Describe Azure Blob storage
- Describe Azure File storage
- Describe Azure Table storage
Part 2: Azure Cosmos DB
Session 11: Features and Use Cases
- Describe capabilities and features of Azure Cosmos DB
- Identify use cases for Azure Cosmos DB
- Describe Azure Cosmos DB APIs
Skill: Describe an Analytics Workload on Azure
Part 1: Large-Scale Analytics
Session 12: Ingestion and Processing
- Describe considerations for data ingestion and processing
- Describe options for analytical data stores
Session 13: Microsoft Analytics Services
- Describe Azure Databricks and Microsoft Fabric for large-scale analytics
Part 2: Real-Time Data Analytics
Session 14: Batch vs. Streaming Data
- Describe the difference between batch and streaming data
- Identify Microsoft cloud services for real-time analytics
Part 3: Data Visualization with Power BI
Session 15: Power BI Capabilities and Features
- Identify capabilities of Power BI
- Describe features of data models in Power BI
Session 16: Visualizations
- Identify appropriate visualizations for data analysis