Learn to extract actionable insights from your big data in real time using a range of Microsoft Azure features

Key Features

  • Updated with the latest features and new additions to Microsoft Azure
  • Master the fundamentals of cloud analytics using Azure
  • Learn to use Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to derive real-time customer insights

Book Description

Cloud Analytics with Microsoft Azure serves as a comprehensive guide for big data analysis and processing using a range of Microsoft Azure features. This book covers everything you need to build your own data warehouse and learn numerous techniques to gain useful insights by analyzing big data

The book begins by introducing you to the power of data with big data analytics, the Internet of Things (IoT), machine learning, artificial intelligence, and DataOps. You will learn about cloud-scale analytics and the services Microsoft Azure offers to empower businesses to discover insights. You will also be introduced to the new features and functionalities added to the modern data warehouse.

Finally, you will look at two real-world business use cases to demonstrate high-level solutions using Microsoft Azure. The aim of these use cases will be to illustrate how real-time data can be analyzed in Azure to derive meaningful insights and make business decisions. You will learn to build an end-to-end analytics pipeline on the cloud with machine learning and deep learning concepts.

By the end of this book, you will be proficient in analyzing large amounts of data with Azure and using it effectively to benefit your organization.

What you will learn

  • Explore the concepts of modern data warehouses and data pipelines
  • Discover unique design considerations while applying a cloud analytics solution
  • Design an end-to-end analytics pipeline on the cloud
  • Differentiate between structured, semi-structured, and unstructured data
  • Choose a cloud-based service for your data analytics solutions
  • Use Azure services to ingest, store, and analyze data of any scale

Who this book is for

This book is designed to benefit software engineers, Azure developers, cloud consultants, and anyone who is keen to learn the process of deriving business insights from huge amounts of data using Azure.

Though not necessary, a basic understanding of data analytics concepts such as data streaming, data types, the machine learning life cycle, and Docker containers will help you get the most out of the book.

Table of Contents

  1. Cloud Analytics with Microsoft Azure, Second Edition
  2. Preface
    1. About Cloud Analytics with Microsoft Azure, Second Edition
    2. About the authors
    3. About the reviewer
    4. Learning objectives
    5. Audience
    6. Approach
    7. Hardware and software requirements
    8. Conventions
    9. Installation and setup
  3. 1. Introducing analytics on Azure
    1. The power of data
    2. Big data analytics
    3. Internet of Things (IoT)
    4. Machine learning
    5. Artificial intelligence (AI)
    6. DataOps
    7. Why Microsoft Azure?
    8. Security
    9. Cloud scale
    10. Top business drivers for adopting data analytics in the cloud
    11. Rapid growth and scale
    12. Reducing costs
    13. Driving innovation
    14. Why do you need a modern data warehouse?
    15. Bringing your data together
    16. Creating a data pipeline
    17. Data ingestion
    18. Data storage
    19. Data pipeline orchestration and monitoring
    20. Data sharing
    21. Data preparation
    22. Data transform, predict, and enrich
    23. Data serve
    24. Data visualization
    25. Smarter applications
    26. Summary
  4. 2. Introducing the Azure Synapse Analytics workspace and Synapse Studio
    1. What is Azure Synapse Analytics?
    2. Why do we need Azure Synapse Analytics?
    3. Customer challenges
    4. Azure Synapse Analytics to the rescue
    5. Deep dive into Azure Synapse Analytics
    6. Introducing the Azure Synapse Analytics workspace
    7. Free Azure account
    8. Quickstart guide
    9. Introducing Synapse Studio
    10. Launching Synapse Studio
    11. Provisioning a dedicated SQL pool
    12. Exploring data in the dedicated SQL pool
    13. Creating an Apache Spark pool
    14. Integrating with pipelines
    15. The Monitor hub
    16. Summary
  5. 3. Processing and visualizing data
    1. Power BI
    2. Features and benefits
    3. Power BI and Azure Synapse Analytics
    4. Features and benefits
    5. Quick start guide (Data modeling and visualization)
    6. Machine learning on Azure
    7. ML.NET
    8. Automated machine learning
    9. Cognitive services
    10. Bot framework
    11. Azure Machine Learning features and benefits
    12. Software Development Kit (SDK)
    13. Designer
    14. AutoML
    15. Flexible deployment targets
    16. Accelerated Machine Learning Operations (MLOps)
    17. Azure Machine Learning and Azure Synapse Analytics
    18. Quick start guide (Azure Machine Learning)
    19. Prerequisites
    20. Creating a machine learning model using Designer
    21. Summary
  6. 4. Business use cases
    1. Use case 1: Real-time customer insights with Azure Synapse Analytics
    2. The problem
    3. Capturing and processing new data
    4. Bringing all the data together
    5. Finding insights and patterns in data
    6. Real-time discovery
    7. Design brainstorming
    8. Data ingestion
    9. Data storage
    10. Data science
    11. Dashboards and reports
    12. The solution
    13. Data flow
    14. Azure services
    15. Azure Data Lake Storage Gen2
    16. Azure Synapse Analytics
    17. Azure Synapse Hybrid Integration (Pipelines)
    18. Power BI
    19. Azure supporting services
    20. Insights and actions
    21. Reducing waste by 18%
    22. Social media trends drive sales up by 14%
    23. Conclusion
    24. Use case 2: Using advanced analytics on Azure to create a smart airport
    25. The problem
    26. Business challenges
    27. Technical challenges
    28. Design brainstorming
    29. Data sources
    30. Data storage
    31. Data ingestion
    32. Security and access control
    33. Discovering patterns and insights
    34. The solution
    35. Why Azure for NIA?
    36. Solution architecture
    37. Azure services
    38. Azure Synapse Analytics
    39. Azure Cosmos DB
    40. Azure Machine Learning
    41. Azure Container Registry
    42. Azure Kubernetes Service
    43. Power BI
    44. Supporting services
    45. Insights and actions
    46. Reducing flight delays by 17% using predictive analytics
    47. Reducing congestion and improving retail using smart visualization
    48. Conclusion
  7. 5. Conclusion
    1. Final words
    2. For further learning
  8. Index