DP-900: Microsoft Azure Data Fundamentals

DP-900: Microsoft Azure Data Fundamentals

Offered by Linux Training

The DP-900: Microsoft Azure Data Fundamentals course at Linux Training is designed for beginners, students, and professionals who want to understand the basics of data concepts and how they are implemented using Microsoft Azure.

This course introduces core data concepts such as relational and non-relational data, analytics workloads, and cloud data services, providing a strong foundation for anyone looking to start a career in data or cloud technologies.

Course Overview

This program provides a foundational understanding of data services in the cloud, helping learners explore how Microsoft Azure supports modern data solutions. It is ideal for those who are new to data, databases, and analytics.

What You Will Learn

  • Core Data Concepts
  • Relational Data in Azure
  • Non-Relational Data in Azure
  • Data Analytics Workloads
  • Introduction to Azure Data Services
  • Basics of Data Visualization

Why Choose This Course?

  • Beginner-friendly certification
  • Entry point into data and cloud careers
  • No prior technical experience required
  • Industry-recognized certification (DP-900)
  • Hands-on introduction to Azure tools

Career Opportunities

After completing this course, you can explore roles such as:

  • Data Analyst (Beginner Level)
  • Cloud Data Associate
  • Database Assistant
  • IT Support Associate
  • Business Intelligence Assistant

Who Can Join?

  • Students and fresh graduates
  • Non-IT and IT beginners
  • Professionals switching to data or cloud careers
  • Anyone interested in Azure and data fundamentals

Start Your Journey in Data and Cloud

Join Linux Training and build a strong foundation in data concepts and Azure services to kickstart your career in the data-driven world.

DP-900: Microsoft Azure Data Fundamentals

Modules

1. Describe core data concepts (25–30%)

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

    • 2. Identify considerations for relational data on Azure (20–25%)

    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

    • 3. Describe considerations for working with non-relational data on Azure (15–20%)

    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

    • 4. Describe an analytics workload (25–30%)

    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

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