Before you wrap up these data visualization sections, you need to be introduced to key performance indicators, or KPIs for short.
A KPI is usually a single value that describes the results of a business indicator, such as the churn rate, net promoter score (NPS), return on investment (ROI), and so on. Although there are some standard indicators across different industries, you usually need to build custom metrics based on the company’s needs.
To be honest, the most complex challenge associated with indicators is not in their visualization aspect itself, but in the way they have been built (the rules used) and the way they will be communicated and used across different levels of the company.
From a visualization perspective, just like any other single value, you can use all those charts that you have learned about to analyze your indicator, depending on your need. However, if you just want to show your KPI, with no time dimension, you can use a widget.
Alright, these are the most important topics about data visualization that you should know for the AWS Certified Machine Learning – Specialty exam. Now, let us have a look at QuickSight, an AWS service where you can implement all these visualization techniques you have just learned.
Amazon QuickSight is a cloud-based analytics service that allows you to build data visualizations and ad hoc analysis. QuickSight supports a variety of data sources, such as Redshift, Aurora, Athena, RDS, and your on-premises database solution.
Other sources of data include S3, where you can retrieve data from Excel, CSV, or log files, and Software-as-a-Service (SaaS) solutions, where you can retrieve data from Salesforce entities.
Amazon QuickSight has two versions:
The most important difference between these two versions is the possibility of integration with Microsoft Active Directory (AD) and encryption at rest. Both features are only provided in the Enterprise edition.
Important note
Keep in mind that AWS services are constantly evolving, so more differences between the Standard and Enterprise versions may crop up in the future. You should always consult the latest documentation of AWS services to check what is new.
In terms of access management, QuickSight offers a very simple interface you can use to control user access. In the Standard edition, you have pretty much two options for inviting a user to your QuickSight account:
If you invite IAM users, then they can automatically log in to your account and see or edit your visualization, depending on the type of permission you have provided during QuickSight user creation. If you have invited an email address, then the email owner has to access their mailbox to complete this operation.
Removing a user is also straightforward. The only extra information you have to provide while removing a user is whether you want to transfer the orphaned resources to another user into your account or delete all the user’s resources.
If you are playing with the Enterprise edition, this process of authorizing users can be a little different, since you have AD working for you. In this case, you can grant access to AD groups, and all the users from those groups will be granted access to your account on QuickSight.
Also, remember that in both versions, all data transfer is encrypted; however, you will only find encryption at rest in the Enterprise edition.
When you are bringing data into QuickSight, you are technically creating what are known as datasets. Datasets, in turn, are imported in an optimized structure into QuickSight, known as the Super-fast, Parallel, In-memory, Calculation Engine (SPICE). That is why QuickSight can perform data visualization on big data.
Finally, you should know that QuickSight does not only allow you to plot your data, but also perform some small data preparation tasks, such as renaming fields, computing new fields, changing data types, preparing queries to retrieve data from the source, and joining tables from the same source.
Summarizing the main steps for working with QuickSight:
That brings you to the end of this chapter about data visualizations! Now, take some time to recap what you have learned.
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