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DP-900: Microsoft Azure Data Fundamentals

Course Summary

DP-900 is a foundational Microsoft certification course that introduces core data concepts and the Microsoft Azure data services ecosystem. It covers how data is represented and stored, the nature of relational and non-relational databases, common data workloads, and cloud-based analytics and visualization tools. 

Prerequisites:

In order to succeed in this course, you will need:

  • Basic familiarity with the concepts of relational and non-relational data is helpful 
  • Familiarity with different types of data workloads (transactional vs. analytical) is beneficial
Purpose
Gain foundational knowledge of core data concepts and related Microsoft Azure data services while preparing for the DP-900 certification exam
Audience
A foundation for IT Professionals entering the data or cloud space 
Role
Software Developers | Business Analysts | Technical Managers
Skill level
Beginner
Style
Lecture | Hands-on Activities | Labs
Duration
1 day
Related technologies
Cloud | Azure | Azure SQL | Cosmos DB | Microsoft Power BI

 

Learning objectives
  • Distinguish between structured, semi-structured, and unstructured data 
  • Differentiate transactional and analytical data workloads
  • Describe the roles of database administrators, data engineers, and data analysts
  • Explain relational data concepts 
  • Identify Azure relational data services 
  • Describe non-relational Azure storage capabilities 
  • Explain large-scale analytics concepts
  • Differentiate batch and streaming data 
  • Identify Azure services for real-time analytics

What you'll learn:

In this DP-900: Microsoft Azure Data Fundamentals course, you'll learn:

Describe Core Data Concepts 

  • Describe ways to represent data
    • Describe features of structured data
    • Describe features of semi-structured
    • Describe features of unstructured data
  • Identify options for data storage
    • Describe common formats for data files
    • Describe types of databases
  • Describe common data workloads
    • Describe features of transactional workloads
    • Describe features of analytical workloads
  • Identify roles and responsibilities for data workloads
    • Describe responsibilities for database administrators
    • Describe responsibilities for data engineers
    • Describe responsibilities for data analysts

Identify Considerations for Relational Data on Azure

  • Describe relational concepts
    • Identify features of relational data
    • Describe normalization and why it is used
    • Identify common structured query language (SQL) statements
    • Identify common database objects
  • Describe relational Azure data services
    • Describe the Azure SQL family of products including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines
    • Identify Azure database services for open-source database systems

Describe Considerations for Working with Non-relational Data on Azure 

  • Describe capabilities of Azure Storage
    • Describe Azure Blob storage
    • Describe Azure File storage
    • Describe Azure Table storage
  • Describe capabilities and features of Azure Cosmos DB
    • Identify use cases for Azure Cosmos DB
    • Describe Azure Cosmos DB APIs

Describe an Analytics Workload 

  • Describe common elements of large-scale analytics
    • Describe considerations for data ingestion and processing
    • Describe options for analytical data stores
    • Describe Microsoft cloud services for large-scale analytics, including Azure Databricks and Microsoft Fabric
  • Describe consideration for real-time data analytics
    • Describe the difference between batch and streaming data
    • Identify Microsoft cloud services for real-time analytics
  • Describe data visualization in Microsoft Power BI
    • Identify capabilities of Power BI
    • Describe features of data models in Power BI
    • Identify appropriate visualizations for data   

DP-900: Microsoft Azure Data Fundamentals

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