Cloud Analytics with Microsoft Azure, Second Edition

Cloud Analytics with Microsoft Azure, Second Edition

Copyright © 2021 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

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Authors: Has Altaiar, Jack Lee, and Michael Peña

Technical Reviewer: Aaditya Pokkunuri

Managing Editors: Aditya Datar and Neha Pande

Acquisitions Editor: Ben Renow-Clarke

Production Editor: Deepak Chavan

Editorial Board: Vishal Bodwani, Ben Renow-Clarke, Arijit Sarkar, Dominic Shakeshaft, and Lucy Wan

First Published: October 2019

Production Reference: 2270121

ISBN: 978-1-80020-243-6

Published by Packt Publishing Ltd.

Livery Place, 35 Livery Street

Birmingham B3 2PB, UK

Table of Contents

Preface

1. Introducing analytics on Azure

The power of data

Big data analytics

Internet of Things (IoT)

Machine learning

Artificial intelligence (AI)

DataOps

Why Microsoft Azure?

Security

Cloud scale

Top business drivers for adopting data analytics in 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 and monitoring

Data sharing

Data preparation

Data transform, predict, and enrich

Data serve

Data visualization

Smarter applications

Summary

2. Introducing the Azure Synapse Analytics workspace and Synapse Studio

What is Azure Synapse Analytics?

Why do we need Azure Synapse Analytics?

Customer challenges

Azure Synapse Analytics to the rescue

Deep dive into Azure Synapse Analytics

Introducing the Azure Synapse Analytics workspace

Free Azure account

Quickstart guide

Introducing Synapse Studio

Launching Synapse Studio

Provisioning a dedicated SQL pool

Exploring data in the dedicated SQL pool

Creating an Apache Spark pool

Integrating with pipelines

The Monitor hub

Summary

3. Processing and visualizing data

Power BI

Features and benefits

Power BI and Azure Synapse Analytics

Features and benefits

Quick start guide (Data modeling and visualization)

Machine learning on Azure

ML.NET

Automated machine learning

Cognitive services

Bot framework

Azure Machine Learning features and benefits

Software Development Kit (SDK)

Designer

AutoML

Flexible deployment targets

Accelerated Machine Learning Operations (MLOps)

Azure Machine Learning and Azure Synapse Analytics

Quick start guide (Azure Machine Learning)

Prerequisites

Creating a machine learning model using Designer

Summary

4. 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 Lake Storage Gen2

Azure Synapse Analytics

Azure Synapse Hybrid Integration (Pipelines)

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 Synapse Analytics

Azure Cosmos DB

Azure Machine Learning

Azure Container Registry

Azure Kubernetes Service

Power BI

Supporting services

Insights and actions

Reducing flight delays by 17% using predictive analytics

Reducing congestion and improving retail using smart visualization

Conclusion

5. Conclusion

Final words

For further learning

Index

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