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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:


  • Course Code:


Who should attend & recommended skills:

Those with basic IT, Linux, machine learning, and Microsoft Azure skills

Who should attend & recommended skills

  • This course is geared for attendees with basic Linux and computing skills who wish to Leverage the power of Azure to get efficient data insights from your big data in real time.
  • Skill-level: Foundation-level Cloud Analytics with Microsoft Azure skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Microsoft Azure: Basic (1-2 years’ experience)
  • Machine Learning: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

With data being generated at an exponential speed, organizations all over the world are migrating their infrastructure to the cloud. Application management becomes much easier when you use a cloud platform to build, manage, and deploy your services and applications. Cloud Analytics with Microsoft Azure covers all that you need to extract useful insights from your data. You’ll explore the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You’ll also delve into data analytics by studying use cases that focus on creating actionable insights from near-real-time data. As you advance, you’ll learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts. By the end of this course, you’ll have developed a solid understanding of data analytics with Azure and its practical implementation.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Cloud Analytics with Microsoft Azure instructor, students will learn about and explore:
  • The basics of cloud analytics using Azure
  • Different ways to process and visualize your data easily
  • Using Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insights
  • Exploring the concepts of modern data warehouses and data pipelines
  • Discovering different design considerations while applying a cloud analytics solution
  • Designing an end-to-end analytics pipeline on the cloud
  • Differentiating between structured, semi-structured, and unstructured data
  • Choosing a cloud-based service for your data analytics solutions
  • Using Azure services to ingest, store and analyze data of any scale

Course breakdown / modules

  • The Power of Data
  • Big Data Analytics
  • Internet of Things (IoT)
  • Machine Learning and Artificial Intelligence
  • DataOps
  • Why Microsoft Azure?
  • Top Business Drivers for Adopting Data Analytics on the Cloud
  • Why Do You Need a Modern Data Warehouse?
  • Creating a Data Pipeline
  • Smarter Applications

  • What is a Modern Data Warehouse?
  • Azure Synapse Analytics
  • Azure Data Factory
  • Azure Data Lake Storage Gen2
  • Azure Databricks
  • Quick Start Guide

  • Azure Analysis Services
  • Power BI
  • Quick Start Guide (Data Modeling and Visualization)
  • Machine Learning on Azure
  • Azure Machine Learning Services Features and Benefits
  • Quick Start Guide (Machine Learning)

  • What is Azure Synapse Analytics?
  • Why do we need Azure Synapse Analytics?
  • The Modern Data Warehouse Pattern
  • Deep Dive into Azure Synapse Analytics
  • New Preview Features
  • Upcoming Changes

  • Use Case 1: Real-Time Customer Insights with Azure Synapse Analytics
  • The Problem
  • Design Brainstorming
  • The Solution
  • Azure Services
  • Insights and Actions
  • Use Case 2: Using Advanced Analytics on Azure to Create a Smart Airport
  • The Problem
  • Design Brainstorming
  • The Solution
  • Azure Services
  • Insights and Actions
  • Conclusion