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Effective Business Intelligence with QuickSight.
From data to actionable business insights using Amazon QuickSight! About This Book A practical hands-on guide to improving your business with the power of BI and Quicksight Immerse yourself with an end-to-end journey for effective analytics using QuickSight and related services Packed with real-worl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Nadipalli, Rajesh. | |
| 245 | 1 | 0 | |a Effective Business Intelligence with QuickSight. |
| 260 | |a Birmingham : |b Packt Publishing, |c 2017. | ||
| 300 | |a 1 online resource (258 pages) | ||
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| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: A Quick Start to QuickSight -- Era of big data -- Current BI landscape -- Key features provided by BI tools -- Typical process to build visualizations -- Key issues with traditional BI tools -- Rise of cloud BI services -- Overview of QuickSight -- How is QuickSight different to other BI tools? -- High level BI solution architecture with QuickSight -- Getting started with QuickSight -- Registering for QuickSight -- Signing up to QuickSight with a new AWS account -- Signing up to QuickSight with an existing AWS account -- Building your first analysis under 60 seconds -- Downloading data -- Preparing data -- QuickSight navigation -- Loading data to QuickSight -- Starting your visualizations -- Building multiple visualizations -- Summary -- Chapter 2: Exploring Any Data -- AWS big data ecosystem -- Collect -- Store -- Analyze -- Orchestrate -- Supported data sources -- Supported data types -- Supported data sizes -- File limits -- Table limits -- Use case review -- Permissions on AWS resources -- Loading text files to QuickSight -- Uploading a data file to S3 -- Building the manifest file -- Creating a new QuickSight dataset from S3 -- Loading MySQL data to QuickSight using the AWS pipeline -- Pre-requisites -- Uploading data to S3 -- Creating and executing the AWS Data Pipeline -- Creating a new QuickSight dataset from MySQL -- Loading Redshift data to QuickSight -- Pre-requisites -- Uploading data to S3 -- Creating and executing an AWS Data Pipeline -- Creating a new QuickSight dataset from Redshift -- Loading data from Athena to QuickSight -- Uploading data to S3 -- Creating a table in Athena -- Creating a new QuickSight dataset from Athena -- Loading data from Salesforce to QuickSight. | |
| 505 | 8 | |a Pre-requisites -- Creating a dataset from Salesforce -- Editing existing datasets -- Summary -- Chapter 3: SPICE up Your Data -- SPICE -- overview and architecture -- Importing data into SPICE -- Joining data in SPICE -- Loading data to Redshift -- Creating a new joined dataset -- Enriching your data -- Arithmetic and comparison operators -- Conditional functions -- ifelse -- coalesce -- isNotNull -- isNull -- nullIf -- Date functions -- epochDate -- formatDate -- now -- dateDiff -- extract and truncDate -- Numeric functions -- ceil -- decimalToInt -- floor -- intToDecimal -- round -- String functions -- concat -- left -- locate -- ltrim -- parseDate -- parseDecimal -- parseInt -- replace -- right -- rtrim -- strlen -- substring -- toLower -- toString -- toUpper -- trim -- Filtering data using SPICE -- Adding new filters -- Filter on medianincome -- Filter on statecode -- Editing existing filters -- Changing existing filter criteria -- Enable, disable, or delete a filter -- Summary -- Chapter 4: Intuitive Visualizations -- From data to visualization using QuickSight -- Building analyses from datasets -- Creating a new dataset -- Creating a new analysis -- Adding a visual to an analysis -- Renaming and adding descriptions to an existing analysis -- Deleting an existing analysis -- Building effective visuals -- Changing visual type -- Bar charts -- Simple bar charts -- Stacked bar charts -- Line charts -- Simple line chart -- Area line chart -- Pivot tables -- Adding statistical functions -- Scatter plot -- Tree map -- Pie chart -- Heat map -- Autograph -- General options -- Configuring the visual title -- Configuring legends -- Configuring the axis range -- Changing visual colors -- Adding drill down to charts -- Selecting the right visualizations -- Does the business want to compare values? -- Do you need to compare compositions of a measure? | |
| 505 | 8 | |a Do you need to see distributions and relationship between two measures? -- Do you want to see trends with multiple measures? -- Do you want to slice and dice multiple measures over different dimensions? -- Deleting a visual -- Telling a story -- Creating a story -- Playing a Story -- Deleting a Story -- Sharing dashboards -- Deleting a dashboard -- Summary -- Chapter 5: Secure Your Environment -- Managing users and access -- Adding new users -- Reactivate a user -- View existing User -- Deleting a user -- Enterprise account user management -- Prerequisites -- Adding AD user accounts to QuickSight -- Deactivating AD accounts with QuickSight -- Managing QuickSight permissions on AWS resources -- Authorizing connections from QuickSight to AWS data sources -- Creating a new security group for QuickSight -- Authorizing connections to RDS instances -- Authorizing connections to Redshift cluster -- Authorizing connections to EC2 instance -- Closing a QuickSight account -- Summary -- Chapter 6: QuickSight Mobile -- Installing QuickSight -- Dashboards on the go -- Dashboard detailed view -- Find your dashboard -- Favorite a dashboard -- Limitations of the mobile app -- Analyses on the go -- View details of your analysis -- Share your analysis -- Stories related to analysis -- Search for analysis -- Favorite your analysis -- Limitations of the mobile app -- Stories on the go -- Story detailed view -- Search your stories -- Favorite a story -- Advanced options for the QuickSight mobile app -- Summary -- Chapter 7: Big Data Analytics Mini Project -- Overview of AWS Data Lake solution -- Data lake core concept -- package -- AWS Data Lake architecture -- Managed data ingestion to AWS Data Lake -- Centralized data storage for AWS Data Lake -- Processing and analyzing data within the AWS Data Lake -- Governing and securing the AWS Data Lake. | |
| 505 | 8 | |a A mini project on AWS Data Lake -- Mini use case business context -- Air quality index -- Census population -- Deploying AWS Data Lake using CloudFormation -- Creating a new stack -- Access your data lake stack -- Acquiring the data for the mini project -- Hydrating the data lake -- Air quality index data in S3 -- US population data in S3 -- Cataloging data assets -- Creating governance tags -- Registering data packages -- EPA AQI data package -- USA population history package -- Searching the data catalog -- Extracting packages using manifest -- Processing data in the AWS Data Lake -- Creating Athena tables -- Analyzing using QuickSight -- Population analysis -- Creating the population dataset -- Insights from population dataset -- Combining population and EPA datasets -- EPA Trend with population impact -- Additional data lake features -- User management for the AWS Data Lake -- Inviting a new user -- Updating an existing user -- General system settings for AWS Data Lake -- Summary -- Chapter 8: QuickSight Product Shortcomings -- QuickSight product features -- Easy ad hoc analysis and visualizations -- Wide range of data connectivity -- Fast and visual data preparation -- Sharing and collaboration -- Security and access -- Easy operations -- Features lacking in QuickSight -- Lack of integration with the visualization layer -- Only basic visualizations -- Limited mobile and sharing -- Lack of advanced data management -- Advanced data preparation features -- Lack of fine grain access -- General -- Accessing the user guide -- Providing feedback -- Summary -- Index. | |
| 520 | |a From data to actionable business insights using Amazon QuickSight! About This Book A practical hands-on guide to improving your business with the power of BI and Quicksight Immerse yourself with an end-to-end journey for effective analytics using QuickSight and related services Packed with real-world examples with Solution Architectures needed for a cloud-powered Business Intelligence service Who This Book Is For This book is for Business Intelligence architects, BI developers, Big Data architects, and IT executives who are looking to modernize their business intelligence architecture and deliver a fast, easy-to-use, cloud powered business intelligence service. What You Will Learn Steps to test drive QuickSight and see how it fits in AWS big data eco system Load data from various sources such as S3, RDS, Redshift, Athena, and SalesForce and visualize using QuickSight Understand how to prepare data using QuickSight without the need of an IT developer Access QuickSight using the mobile application Architect and design for AWS Data Lake Solution, leveraging AWS hosted services Build a big data project with step-by-step instructions for data collection, cataloguing, and analysis Secure your data used for QuickSight from S3, RedShift, and RDS instances Manage users, access controls, and SPICE capacity In Detail 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 t ... | ||
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