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Data Fundamentals for Cloud Computing with Microsoft Azure

Batch Price From £300 (approx. $381 USD) View Dates & Prices Short course on Microsoft Azure Cloud Computing
Total Duration: 10 Hours
Course level: Beginner
Delivery Method: Instructor Led Classroom Training
Certification: Certificate of Completion will be provided after completing the course

Course Overview

This is a comprehensive course intended for professionals interested in the fundamentals of data in Azure including relational and non-relational data concepts, Azure services for DB, large-scale analytics, and real-time data analytics.

At the end of the course, learners should be comfortable creating & consuming data within the Microsoft Azure system. This learning path helps prepare you for exam DP-900: Microsoft Azure Data Fundamentals.


Familiarity with basic data terms and concepts will be helpful for learners but it is not compulsory.

Course Dates, Prices & Enrolment

All Training Physical Classes Virtual Classes
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Course Content

  1. Introduction to Azure Data
    • Understanding Data Structure
    • Data Storage Options
    • Transactional & Analytical Workloads
    • Roles & Responsibilities for Data Professional
  2. Relational Data Concepts
    • Understanding Relational Data
    • Understanding Normalization
    • Using SQL Statements
    • Overview of Database Objects
  3. Azure Services for Databases
    • SQL Server on Azure VM
    • Azure SQL Managed Instance
    • Azure SQL Database
    • Azure SQL Edge
    • Azure Services for Open-Source DB
  4. Azure Storage for non-relational Data
    • Azure Blob Storage
    • Azure File Storage
    • Azure Table Storage
    • Azure Cosmos DB
  5. Large-Scale Analytics in Azure
    • Data Ingestion and Processing
    • Analytical Data Stores
    • Azure Services for Data Warehousing
  6. Real-Time Data Analytics in Azure
    • Batch and Stream Processing
    • Technologies for Real-Time Analytics
  7. Data Visualization with Azure
    • Understanding Power BI
    • Core Concepts of Data Modeling
    • Data Visualization Using Models

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