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Cloud Analytics with Microsoft Azure

Cloud Analytics with Microsoft Azure

Has Altaiar, Jack Lee, Michael Pena

Kup pozycję na ebookpoint.pl

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 book, you'll have developed a solid understanding of data analytics with Azure and its practical implementation.

  • Preface
    • About Cloud Analytics with Microsoft Azure
      • About the Authors
      • Learning Objectives
      • Audience
      • Approach
      • Hardware Requirements
      • Software Requirements
      • Conventions
      • Installation and Setup
  • 1. Introducing Analytics on Azure
    • The Power of Data
    • Big Data Analytics
    • Internet of Things (IoT)
    • Machine Learning and Artificial Intelligence
    • DataOps
    • Why Microsoft Azure?
      • Security
      • Cloud Scale
    • Top Business Drivers for Adopting Data Analytics on the Cloud
      • Rapid Growth and Scale
      • Reducing Costs
      • Driving Innovation
    • Why Do You Need a Modern Data Warehouse?
      • Bringing Your Data Together
    • Creating a Data Pipeline
      • Data Ingestion
      • Data Storage
      • Data Pipeline Orchestration & Monitoring
      • Data Sharing
      • Data Preparation
      • Data Transform, Predict, & Enrich
      • Data Serve
      • Data Visualization
    • Smarter Applications
    • Summary
  • 2. Building Your Modern Data Warehouse
    • What is a Modern Data Warehouse?
    • Azure Synapse Analytics
      • Features
      • Benefits
    • Azure Data Factory
      • Features
      • Benefits
    • Azure Data Lake Storage Gen2
      • Features
      • Benefits
    • Azure Databricks
      • Features
      • Benefits
    • Quick Start Guide
      • Provision Your First Azure Synapse Analytics (formerly SQL DW)
      • Querying the Data
      • Whitelisting Your Client IP Address to Access Your Azure Synapse Analytics (formerly SQL DW)
      • Pause your Azure Synapse Analytics when not in use
      • Provisioning your Azure Data Factory
      • Provision Your Azure Data Lake Storage Gen2
      • Integrating Azure Data Factory with Azure Data Lake Storage Gen2
      • Review the result in Azure Data Lake Storage Gen2
      • Provisioning your Azure Databricks Service
      • Using Azure Databricks to Prepare and Transform Data
      • Clean Up Your Azure Synapse Analytics
    • Summary
  • 3. Processing and Visualizing Data
    • Azure Analysis Services
      • SQL Server Analysis Services
      • Features and Benefits
    • Power BI
    • Quick Start Guide (Data Modeling and Visualization)
      • Prerequisites
      • Provisioning the Azure Analysis Service
      • Allowing Client Access
      • Creating a Model
      • Opening the Created Model with Power BI
      • Visualizing Data
      • Publishing the Dashboard
    • Machine Learning on Azure
      • ML.NET
      • AutoML
      • Azure Machine Learning Studio
      • Azure Databricks
      • Cognitive Services
      • Bot Framework
    • Azure Machine Learning Services Features and Benefits
      • Software Development Kit (SDK)
      • Visual Interface
      • AutoML
      • Flexible Deployment Targets
      • Accelerated ML Operations (MLOps)
    • Quick Start Guide (Machine Learning)
    • Summary
  • 4. Introducing Azure Synapse Analytics
    • What is Azure Synapse Analytics?
    • Why do we need Azure Synapse Analytics?
    • The Modern Data Warehouse Pattern
      • Customer Challenges
      • Azure Synapse Analytics Comes to the Rescue
    • Deep Dive into Azure Synapse Analytics
      • Azure Synapse Analytics Workspaces
      • Azure Synapse Analytics Studio
    • New Preview Features
      • Apache Spark
      • SQL On-Demand
      • Data Integration
      • Multiple language support
    • Upcoming Changes
    • Summary
  • 5. Business Use Cases
    • Use Case 1: Real-Time Customer Insights with Azure Synapse Analytics
    • The Problem
      • Capturing and Processing New Data
      • Bringing All the Data Together
      • Finding insights and Patterns in Data
      • Real-time Discovery
    • Design Brainstorming
      • Data Ingestion
      • Data Storage
      • Data Science
      • Dashboards and Reports
    • The Solution
      • Data Flow
    • Azure Services
      • Azure Data Factory
      • Apache Kafka on Azure HDInsight
      • Azure Data Lake Storage Gen2
      • Azure Databricks
      • Azure Synapse Analytics
      • Power BI
      • Azure Supporting Services
    • Insights and Actions
      • Reducing Waste by 18%
      • Social Media Trends Drive Sales up by 14%
    • Conclusion
    • Use Case 2: Using Advanced Analytics on Azure to Create a Smart Airport
    • The Problem
      • Business Challenges
      • Technical Challenges
    • Design Brainstorming
      • Data Sources
      • Data Storage
      • Data Ingestion
      • Security and Access Control
      • Discovering Patterns and Insights
    • The Solution
      • Why Azure for NIA?
      • Solution Architecture
    • Azure Services
      • Azure Databricks
      • Azure Cosmos DB
      • Azure Machine Learning Services
      • Azure Container Registry
      • Azure Kubernetes Service (AKS)
      • Power BI
      • Supporting Services
    • Insights and Actions
      • Reducing Flight Delays by 17% Using Predictive Analytics
      • Reducing Congestion and Improving Retail Using Smart Visualization
    • Conclusion
  • 6. Conclusion
    • Azure Modern Data Warehouse Life Cycle
      • Ingesting the Data
      • Storing the Data
      • Preparing and Training the Data
      • Modeling and Serving the Results
      • Visualization and More
    • Summary
  • Index