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by Rajesh Nadipalli
Effective Business Intelligence with QuickSight
Effective Business Intelligence with QuickSight
Effective Business Intelligence with QuickSight
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
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
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
Pre-requisites
Creating a dataset from Salesforce
Editing existing datasets
Summary
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
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?
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
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
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
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
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
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
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Prev
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Cover
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Effective Business Intelligence with QuickSight
Table of Contents
Effective Business Intelligence with QuickSight
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
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
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
Pre-requisites
Creating a dataset from Salesforce
Editing existing datasets
Summary
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
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?
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
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
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
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
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
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
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