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Home > Data > Business Intelligence > Effective Business Intelligence with QuickSight
Effective Business Intelligence with QuickSight
Effective Business Intelligence with QuickSight

Effective Business Intelligence with QuickSight: Boost your business IQ with Amazon QuickSight

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Effective Business Intelligence with QuickSight

Chapter 2. Exploring Any Data

QuickSight can analyze data from various sources including Amazon Web Services (AWS) data stores, files in common formats, 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. The following topics will be covered:

  • Amazon's big data ecosystem
  • QuickSight-supported data sources
  • QuickSight-supported data types and data sizes
  • Use case review
  • Uploading your own data from files, RDBMS, and SaaS to QuickSight
  • Editing existing datasets
  • Uploading data using Athena

AWS big data ecosystem

Amazon's big data ecosystem has several software services that enable business insights from data. These services can be broadly classified into four major categories - Collect, Store, Analyze, and Orchestrate, as shown in the following diagram:

AWS big data ecosystem

Figure 2.1: AWS big data ecosystem

Let's look at each category in detail.

Collect

The first step for any BI initiative is to collect data from external systems to Amazon for which AWS has the following services:

  • Direct connect: With direct connect, you can establish private connectivity between AWS and your enterprise data center and provide an easy way to move data files from your applications to AWS for analysis
  • Snowball: Snowball (also known as Import/Export) lets you import hundreds of terabytes of data quickly into AWS using Amazon-provided, secure appliances for secure transport
  • Kinesis and Kinesis Firehose: Kinesis services enable building custom applications that process or analyze streaming data

Store

The...

Supported data sources

QuickSight supports broadly three types of data sources: relational, file, and SaaS. For relational sources it supports Athena, RDS, Redshift, MySQL, and SQL Server sources. For file sources, it supports Excel, CSV, and common log formats in S3. For SaaS it supports Salesforce.

Before we explore data sources, I would like the readers to get familiar with QuickSight concept of data source and dataset. A data source identifies sources like relational database, S3 filesystem, and SaaS system like SAP. A dataset identifies specific data in a data source, for example, a table is a dataset in a RDBMS data source. A dataset is imported into SPICE for fast access.

Now let's explore the list of supported data sources using the following steps:

  1. From the QuickSight home page click on Manage data, which will show a list of existing datasets and a summary of your current SPICE utilization, as shown in the following screenshot:

    Supported data sources

    Figure 2.2: Adding a new dataset

  2. Next from this...

Supported data types

QuickSight supports the following primitive data types: date, float, integer, and string. If you load data from files and/or databases that contain fields that cannot be implicitly converted to the preceding primitive data types, ensure that they are explicitly converted before they are imported to QuickSight. The following table lists the source data types for specific database engines:

Database type

Numeric data types

String data types

Date-time data types

Boolean data types

SQL Server

Bigint

Decimal

Float

Int

Money

Numeric

Real

Smallint

Smallmoney

Tinyint

Char

Nchar

Nvarchar

Text

Varchar

Date

Datetime

Datetime2

Datetimeoffset

Smalldatetime

bit

Aurora, MariaDB, and MySQL

Bigint

Decimal

Float

Int

Integer

mediumint

numeric

Smallint

Tinyint

Binary

Blob

Char

Enum

Set

Text

Varbinary

Varchar

Date

Datetime

Timestamp

Year

Tinyint

PostgreSQL

Bigint...

Supported data sizes

Amazon QuickSight uses SPICE in-memory caching and hence there are some limits to data from files and tables. You can see the amount of SPICE you are using by clicking on Manage data in the top-right corner. Now let's look at the file and table limits in detail.

File limits

Any single file uploaded to QuickSight directly or from S3 must be 1 GB or less. If multiple files are imported from S3, the total size of files specified in the manifest file must be less than 5 GB and the total number of files should not exceed 100. The number of columns in a file should not exceed 200 and the number of characters per row should be less than 25,400.

Table limits

Any table or query result set imported into SPICE must be less than 10 GB. If you are dealing with tables with larger datasets, use filters to reduce the number of records to analyze. Data in any string column must be 511 characters or less.

AWS big data ecosystem

Amazon's big data ecosystem has several software services that enable business insights from data. These services can be broadly classified into four major categories - Collect, Store, Analyze, and Orchestrate, as shown in the following diagram:

Figure 2.1: AWS big data ecosystem

Let's look at each category in detail.

Collect

The first step for any BI initiative is to collect data from external systems to Amazon for which AWS has the following services:

  • Direct connect: With direct connect, you can establish private connectivity between AWS and your enterprise data center and provide an easy way to move data files from your applications to AWS for analysis

  • Snowball: Snowball (also known as Import/Export) lets you import hundreds of terabytes of data quickly into AWS using Amazon-provided, secure appliances for secure transport

  • Kinesis and Kinesis Firehose: Kinesis services enable building custom applications that process or analyze streaming data

Store

The data collected...

Supported data sources

QuickSight supports broadly three types of data sources: relational, file, and SaaS. For relational sources it supports Athena, RDS, Redshift, MySQL, and SQL Server sources. For file sources, it supports Excel, CSV, and common log formats in S3. For SaaS it supports Salesforce.

Before we explore data sources, I would like the readers to get familiar with QuickSight concept of data source and dataset. A data source identifies sources like relational database, S3 filesystem, and SaaS system like SAP. A dataset identifies specific data in a data source, for example, a table is a dataset in a RDBMS data source. A dataset is imported into SPICE for fast access.

Now let's explore the list of supported data sources using the following steps:

  1. From the QuickSight home page click on Manage data, which will show a list of existing datasets and a summary of your current SPICE utilization, as shown in the following screenshot:

    Figure 2.2: Adding a new dataset

  2. Next from this page ...

Supported data types

QuickSight supports the following primitive data types: date, float, integer, and string. If you load data from files and/or databases that contain fields that cannot be implicitly converted to the preceding primitive data types, ensure that they are explicitly converted before they are imported to QuickSight. The following table lists the source data types for specific database engines:

Database type

Numeric data types

String data types

Date-time data types

Boolean data types

SQL Server

Bigint

Decimal

Float

Int

Money

Numeric

Real

Smallint

Smallmoney

Tinyint

Char

Nchar

Nvarchar

Text

Varchar

Date

Datetime

Datetime2

Datetimeoffset

Smalldatetime

bit

Aurora, MariaDB, and MySQL

Bigint

Decimal

Float

Int

Integer

mediumint

numeric

Smallint

Tinyint

Binary

Blob

Char

Enum

Set

Text

Varbinary

Varchar

Date

Datetime

Timestamp

Year

Tinyint

PostgreSQL

Bigint

...

Supported data sizes

Amazon QuickSight uses SPICE in-memory caching and hence there are some limits to data from files and tables. You can see the amount of SPICE you are using by clicking on Manage data in the top-right corner. Now let's look at the file and table limits in detail.

File limits

Any single file uploaded to QuickSight directly or from S3 must be 1 GB or less. If multiple files are imported from S3, the total size of files specified in the manifest file must be less than 5 GB and the total number of files should not exceed 100. The number of columns in a file should not exceed 200 and the number of characters per row should be less than 25,400.

Table limits

Any table or query result set imported into SPICE must be less than 10 GB. If you are dealing with tables with larger datasets, use filters to reduce the number of records to analyze. Data in any string column must be 511 characters or less.

Use case review

For the next few chapters, let's bring in a real-life use case and see how QuickSight can help. For this use case, we will analyze college ratings from the Department of Education (https://www.ed.gov) and combine information from Census. The following diagram shows the overall data intake flow. In the subsequent sections, I will go into detail on each of the individual data intake processes:

Figure 2.4: Use case data sources

Next, we will look into loading data from these various sources to QuickSight.

Permissions on AWS resources

Before we get started on loading data from the various sources, we first need to grant permissions to QuickSight to access your AWS resources. Follow these steps to grant access:

  1. From the QuickSight home page, select Manage QuickSight.

  2. Click on Account settings on the left-hand side menu.

  3. Click on Edit AWS permissions as follows:

    Figure 2.5: AWS account permissions

  4. This will show the Edit QuickSight read-only access to AWS resources, as shown in the next screenshot. From here you can grant/revoke access to QuickSight from Redshift, RDS, S3, and Athena sources. After you make changes, do remember to click on Apply.

    Figure 2.6: Grant access to all sources

Now we can start loading data from various sources and create new datasets in QuickSight.

Loading text files to QuickSight

The easiest way to load data to QuickSight is to just upload it to SPICE, which was explained in Chapter 1, A Quick Start to QuickSight. Another alternative to uploading text files is to use S3 storage and this section will detail this flow.

Uploading a data file to S3

For this demonstration, we will use the file USAStateAbbr.csv, which has a list of USA state codes and the corresponding full names.

Note

The sample files are in GitHub at https://github.com/rnadipalli/QuickSight/tree/master/sampledata.

Here are the detailed steps to upload a file to an S3 filesystem:

  1. Download the file to your local system (laptop).

  2. Upload the file to AWS S3, login to our account, and from the Services menu select S3.

  3. Select the S3 bucket or create a new S3 bucket. In the next screenshot, I have selected the collegescorecard bucket that I created earlier.

  4. Click on the Upload button, select the local file from your system, and then upload.

    Figure 2.7: Upload text file to S3

  5. Now that...

Loading MySQL data to QuickSight using the AWS pipeline

In this section, we will look into the data flow path from https://www.ed.gov/ to QuickSight that uses a MySQL database. The source data is obtained from the public site and provides information about colleges in the USA.

The path to get to QuickSight involves the following steps:

  1. Uploading data to S3.

  2. Creating an AWS Data Pipeline to load data from S3 to MySQL.

  3. Loading data from MySQL to QuickSight.

Pre-requisites

The following are the pre-requisites to load data from MySQL to QuickSight:

  • Must have an RDS instance created. In this example, I will show an RDS MySQL instance.

  • Data file must be CSV. It should not contain any header rows.

  • You must have a database username and password that can connect to the database from QuickSight with the SELECT permission on some system tables so that QuickSight can estimate the table size. The following table identifies the system tables that the user account needs permission to select:

    Database type

    Access...

Loading Redshift data to QuickSight

In this section, we will look into the data flow path from the US census to QuickSight using a Redshift data store. The source data is obtained from the public site and provides information about household income and population in the USA by zip code. The path to get to QuickSight involves the following steps:

  1. Uploading data to S3.

  2. Creating an AWS Data Pipeline to load data from S3 to Redshift.

  3. Loading data from Redshift to QuickSight.

Pre-requisites

The following are the pre-requisites to load data from Redshift to QuickSight:

  • Must have a Redshift instance created.

  • Data file must be a CSV. It cannot contain a header in the data file.

  • You must have a database username and password that can connect to the database from QuickSight with the SELECT permission on some system tables so that QuickSight can estimate the table size. These tables are pg_stats, pg_class, and pg_namespace.

Uploading data to S3

For this demonstration, we will use the file USACensusSalarybyZip...

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Key benefits

  • 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

Description

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. Grow your profits, improve your products, and beat your competitors.

Who is this book 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
  • Build interactive charts, reports, dashboards, and storyboards using QuickSight
  • 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

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 10, 2017
Length: 262 pages
Edition : 1st
Language : English
ISBN-13 : 9781786465009
Category :
Data
Concepts :
Business Intelligence
Tools :
QuickSight

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Product Details

Publication date : Mar 10, 2017
Length: 262 pages
Edition : 1st
Language : English
ISBN-13 : 9781786465009
Category :
Data
Concepts :
Business Intelligence
Tools :
QuickSight

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Table of Contents

8 Chapters
1. A Quick Start to QuickSight Chevron down icon Chevron up icon
1. A Quick Start to QuickSight
Era of big data
Current BI landscape
Typical process to build visualizations
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
Building your first analysis under 60 seconds
Summary
2. Exploring Any Data Chevron down icon Chevron up icon
2. Exploring Any Data
AWS big data ecosystem
Supported data sources
Supported data types
Supported data sizes
Use case review
Permissions on AWS resources
Loading text files to QuickSight
Loading MySQL data to QuickSight using the AWS pipeline
Loading Redshift data to QuickSight
Loading data from Athena to QuickSight
Loading data from Salesforce to QuickSight
Editing existing datasets
Summary
3. SPICE up Your Data Chevron down icon Chevron up icon
3. SPICE up Your Data
SPICE - overview and architecture
Importing data into SPICE
Joining data in SPICE
Enriching your data
Filtering data using SPICE
Summary
4. Intuitive Visualizations Chevron down icon Chevron up icon
4. Intuitive Visualizations
From data to visualization using QuickSight
Building analyses from datasets
Building effective visuals
Deleting a visual
Telling a story
Sharing dashboards
Deleting a dashboard
Summary
5. Secure Your Environment Chevron down icon Chevron up icon
5. Secure Your Environment
Managing users and access
Managing QuickSight permissions on AWS resources
Authorizing connections from QuickSight to AWS data sources
Closing a QuickSight account
Summary
6. QuickSight Mobile Chevron down icon Chevron up icon
6. QuickSight Mobile
Installing QuickSight
Dashboards on the go
Analyses on the go
Stories on the go
Advanced options for the QuickSight mobile app
Summary
7. Big Data Analytics Mini Project Chevron down icon Chevron up icon
7. Big Data Analytics Mini Project
Overview of AWS Data Lake solution
AWS Data Lake architecture
A mini project on AWS Data Lake
Additional data lake features
Summary
8. QuickSight Product Shortcomings Chevron down icon Chevron up icon
8. QuickSight Product Shortcomings
QuickSight product features
Features lacking in QuickSight
Accessing the user guide
Providing feedback
Summary

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(2 Ratings)
5 star 50%
4 star 0%
3 star 0%
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1 star 50%
A. Zubarev Mar 16, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A great book for those who want to explore ways to depart from the legacy Business Intelligence and explore how QuickSight fits on its own or as part of the vast Amazon Web Services ecosystem.Reading this book first hand has helped me to dispel my fears of the Business Intelligence being a stranger to the Cloud. Turned out, it is quite the opposite, yet, I am personally thrilled now about what is coming out of the Cloud next and what it is ultimately capable of.The author delivers the content in a calm and friendly manner, and provides with info on the latest additions to the product, as well as its road map.The technical level of the book does not require a reader to be at an intimate level with any specific Business Intelligence offerings.Grab a copy and see where and how it may fit in your organization.
Amazon Verified review Amazon
Manwender Singh Nov 21, 2023
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Not worth investing
Amazon Verified review Amazon

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About the author

Profile icon Nadipalli
Nadipalli
Rajesh Nadipalli is currently a Sr. Director at PPD Enterprise data group and is responsible for empowering data and analytics-driven insights for clinical research and life science use cases. He is leading the next generation cloud data lake with governance, Machine Learning, AI and advanced analytics. In his previous role, he was Director of Professional Services and Support at Zaloni; an award-winning provider of enterprise Data Lake management solutions that enables global clients to innovate and leverage big data for business impact. Raj led big data technical proof-of-concept, solution architecture and post-sales product support for clients that included AIG, NBCU, Verizon, Du, American Express, Netapp and Dell EMC. Throughout his 20+ years of IT experience, Raj has had a passion for data and held various roles as Big Data Architect, DBA, business intelligence architect, and ETL developer. He is also the author of Effective Business Intelligence with QuickSight and HDInsight Essentials. Raj is also a regular blogger and his articles are published in Zaloni blog, Youtube, Dataflow and DBZone. Raj holds an MBA from North Carolina State University and a BS in EE from the University of Mumbai. https://www.linkedin.com/in/nadipalli/
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