Cargando…
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.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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?
- 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.
- 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.