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- Herausgeber: Packt Publishing
- Kategorie: Lebensstil
- Sprache: Englisch
Amazon QuickSight is the next-generation Business Intelligence (BI) cloud service that can help you build interactive visualizations on top of various data sources hosted on Amazon Cloud Infrastructure. QuickSight delivers responsive insights into big data and enables organizations to quickly democratize data visualizations and scale to hundreds of users at a fraction of the cost when compared to traditional BI tools.
This book begins with an introduction to Amazon QuickSight, feature differentiators from traditional BI tools, and how it fits in the overall AWS big data ecosystem. With practical examples, you will find tips and techniques to load your data to AWS, prepare it, and finally visualize it using QuickSight. You will learn how to build interactive charts, reports, dashboards, and stories using QuickSight and share with others using just your browser and mobile app.
The book also provides a blueprint to build a real-life big data project on top of AWS Data Lake Solution and demonstrates how to build a modern data lake on the cloud with governance, data catalog, and analysis. It reviews the current product shortcomings, features in the roadmap, and how to provide feedback to AWS.
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Seitenzahl: 176
Veröffentlichungsjahr: 2017
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Table of Contents
Effective Business Intelligence with QuickSight
Effective Business Intelligence with QuickSight
Copyright © 2017 Packt Publishing
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First published: March 2017
Production reference: 1030317
Published by Packt Publishing Ltd.
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ISBN 978-1-78646-636-5
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Credits
Author
Rajesh Nadipalli
Copy Editors
Vikrant Phadkay
Safis Editing
Reviewer
Arthur Zubarev
Project Coordinator
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Commissioning Editor
David Barnes
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Safis Editing
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Indexer
Tejal Daruwale Soni
Content Development Editor
Aishwarya Pandere
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Disha Haria
Technical Editor
Dinesh Pawar
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About the Author
Rajesh Nadipalli is currently Director, Professional Services and Support at Zaloni, an award-wining provider of enterprise data lake management solutions that enables global clients to innovate and leverage big data for business impact. Rajesh leads Hadoop-based technical proof-of-concepts, strategy, solution architectures, and post-sales product support for his clients. His clientele includes AIG, NBCU, Verizon, Du, American Express, Netapp, Dell-EMC, United Health Group, and Cisco. In his previous role as the director of product management, he was leading the product strategy, roadmap, and feature definitions for Zaloni's Hadoop data management platform.
Throughout his 20 plus years in IT, Rajesh has had a passion for data and held various roles as big data architect, solutions architect, database administrator (DBA), business intelligence architect, and ETL developer. He believes in using technology as a strategic advantage for his clients by improving productivity, performance, and real-time insight to relevant data.
Rajesh is also the author of HDInsight Essentials, by Packt publishing, which takes you through the journey of building a modern data lake architecture using HDInsight, a Hadoop-based service that allows you to successfully manage high volume and velocity data in Azure Cloud.
He is a regular blogger and his articles are published in Zaloni blog, Datafloq, and Dzone sites.
He holds a MBA from North Carolina State University and a BS in EE from University of Mumbai, India.
I would like to thank my family and friends for their love, support, and encouragement; and special thanks to my wife, Manasa, and daughter, Geetika, who were always there for support and inspiration.
About the Reviewer
Arthur Zubarev is a data platform engineer who specializes in developing fast data pipelines, data acquisition, analysis, validation, performance tuning, data warehousing, and visualizations. His skills and passion include relational and NoSQL database engines, big data, cloud technologies, streaming data, IoT, social web, and general programming.
Arthur is a data platform MVP for the sixth year, a member of the Data Advisors group for Microsoft, R for Visual Studio beta tester, MCDBA, MCAD, and MCITP. He strives to be an early adopter of new technologies and likes to embrace technical challenges. In addition, he is a frequent presenter and event organizer.
I would like to thank my wonderful family for always understanding the importance of my contributions to the technical community, even if that occurred at the expense of our spare time. This has allowed me to effectively deliver as much timely feedback as I could to produce this fantastic book.
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Preface
We live in an era where data defines business and is also growing exponentially, specifically in the cloud. Organizations that can empower their business with easy-to-use, fast, and real-time data will have a competitive edge over their peers; it is estimated that such organizations will save over $430 billion by 2020 compared to their peers.
Amazon QuickSight is an innovative next-generation cloud-powered BI service that makes it easy for anyone to build visualizations, perform ad hoc analysis, and quickly get business insights from their data. QuickSight delivers fast and responsive insights on big data and can scale to hundreds of users at a fraction of the cost when compared to traditional BI tools.
This practical example-rich guide begins by introducing you to Amazon QuickSight and reviewing what makes it unique, and explains how to get started building your first analysis on QuickSight. Moving ahead, you will get to know the entire AWS big data ecosystem, right from ingesting the data into various storage services and then use QuickSight's features to gain insights. We will next review how to perform lightweight transformations of the data within QuickSight in order to easily enrich your data and insights. Next, we will check out the various visualizations supported by QuickSight including analyses, dashboards, and story features that enable collaboration with your peers. We will next look at the QuickSight mobile application that empowers you with dashboards on the go. We will next look at how to build an end-to-end architecture for big data analytics using AWS Data Lake solution, which packages the most commonly needed components to jump-start such projects. Towards the end, you will learn what features the product is lacking and what's on the roadmap.
Throughout the book, you will be guided with step-by-step instructions, screenshots, data flow, and architecture models that you can reuse for your initiatives.
What this book covers
Chapter 1, A Quick Start to QuickSight, gives an overview of Amazon QuickSight and how it differs to traditional BI tools.
Chapter 2, Exploring Any Data, explains that QuickSight can analyze data from various sources including AWS data stores, files in common format, Salesforce, and popular database engines. QuickSight has a simple interface to connect to these sources and create datasets from them that can be stored in SPICE for subsequent analysis. In this chapter, we will first look at Amazon's big data ecosystem and then review how QuickSight can be used to connect to the various data stores.
Chapter 3, SPICE up Your Data, explores SPICE which is the accelerator of QuickSight, delivering interactive visualizations on large data sets in less than 60 seconds. SPICE is engineered with parallelism, automatic replications, and a rich calculation engine to serve thousands of users who can simultaneously perform fast interactive queries.
Chapter 4, Intuitive Visualizations, looks at visualization capabilities in detail. QuickSight can create a wide variety of visuals on different datasets imported to SPICE.
Chapter 5, Secure Your Environment, explains that to secure your BI environment you need to control which users have access to QuickSight and also what resources QuickSight has permissions to read.
Chapter 6, QuickSight Mobile, covers the QuickSight iOS mobile app. It allows you to stay connected to your data from anywhere, anytime, on your iPhone, iPad, or iPod touch. You can visualize, explore, and share your analyses, dashboards, and stories with an intuitive user experience and get answers to business questions in your palm.
Chapter 7, Big Data Analytics Mini Project, has a real-life use case leveraging the AWS Data Lake solution. Modern data architectures are moving to a data lake solution that has the ability to ingest data from various sources, transform, and analyze at big data scale. Amazon now offers a data lake solution that packages the most commonly needed big data components along with a web application to jump-start the data lake build out.
Chapter 8, QuickSight Product Shortcomings, covers some shortcomings of QuickSight. While the product is revolutionary and has a bold vision, there are several shortcomings in the current version for it to replace enterprise solutions, which are discussed in this final chapter.
What you need for this book
To follow the exercises in the book, you will need the following:
Who this book is for
This book is for all business professionals who have reporting, data analysis, and dashboard needs on a cloud-hosted Amazon service. It is also written for big data architects, enterprise architects, and business leaders involved in strategy who want to advance their organization's BI capabilities and improve overall business profitability.
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{ "Statement": [ { "Action": [ "iam:ListPolicyVersions", "iam:ListAccountAliases", "iam:AttachRolePolicy", "iam:GetPolicy", ] } ] }New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Next select the STABBR as the Y axis and TUITFTE as the Value field."
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Tip
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