Getting Started: Amazon QuickSight (BI Tool) Part 2

This blog was written by Ayesha Mangera of OptimalBI and published with their permission. This is part 2 of 2 blogs on getting started with AWS QuickSight. Part 1 can be found here. She also wrote a comparison between QuickSight and PowerBI worth a read.

Amazon QuickSight is transforming how businesses visualise and interpret data. This cloud-native BI tool empowers users of all skill levels to create impactful dashboards and reports with ease.

In this blog, we'll explore QuickSight's key features - from data preparation to interactive visualisations and collaborative sharing. Whether you're a data novice or a seasoned analyst, join us as we unlock the potential of your data and elevate your decision-making process with Amazon QuickSight.‍

What is QuickSight?

Amazon QuickSight is a cloud-scale business intelligence (BI) service that you can use to deliver easy-to-understand insights to the people who you work with, wherever they are. It connects to your data in the cloud and combines data from many different sources.

In a single data dashboard, QuickSight can include AWS data, third-party data, big data, spreadsheet data, SaaS data, B2B data, and more. As a fully managed cloud-based service, Amazon QuickSight provides enterprise-grade security, global availability, and built-in redundancy. 

QuickSight gives decision-makers the opportunity to explore and interpret information in an interactive visual environment. They have secure access to dashboards from any device on your network and from mobile devices.

Dataset

This blog leverages a comprehensive Sales dataset in CSV format, encompassing crucial business dimensions such as geographic locations, time periods, store information, product hierarchies (categories and subcategories), and promotional data. This rich, multi-faceted dataset serves as an ideal playground for exploring QuickSight's diverse visualisation and analysis capabilities, mirroring the complexity of real-world business scenarios.‍

Data Sources 

Amazon QuickSight is a versatile tool that can handle a wide range of data sources for analyses. It supports popular platforms such as Snowflake, Amazon Athena, Amazon Redshift, Amazon S3, Microsoft SQL Server, and Oracle, giving you the flexibility to work with the data sources you are familiar with.

Loading Data into QuickSight

Once you sign in to AmazonQuicksight using your credentials, it will open the Analyses screen. Here, you can use Sample data or create a new dataset with your preferred connector.

Go to Datasets from the left pane and select the New dataset button: 

This will take you to a new screen. As we are uploading multiple CSV files, let’s click on Upload a file tile.

Once first file uploaded, click on three dots (…), select use in a new dataset

Rename the dataset with your preferred name. Then select Add data button and upload rest of the files. Please note that each file will act as a table.

Editing Dataset

Once all the files are uploaded, let’s create relationship between the tables. 

join is performed between two QuickSight logical tables, where each logical table contains information about how to fetch data. When editing a dataset in QuickSight, the join diagram at the top half of the page shows each logical table as a rectangular block.

An icon with red dots appears to indicate that you need to configure this join. Two red dots appear for joins that aren't yet configured. To create joins, choose the first join configuration icon.

If the tables that you selected join on multiple columns, choose Add a new join clause. Doing this adds another row to the join clauses, so you can specify the next set of columns to join. Repeat this process until you have identified all of the join columns for the two data objects.

You can rearrange the tables by dragging and dropping them until rectangular block turns green.

In the Fields section, you can use each field's menu to do one or more of the following:

  • Add a hierarchy to a geospatial field.

  • Include or Exclude the field.

  • Edit name & description of the field.

  • Change data type.

  • Add a calculation (a calculated field).

  • Restrict access to only me, so only you can see it. This can be helpful when you are adding fields to a dataset that's already in use.

In the Filters section, you can add or edit filters.

Reverting Dataset

When you save and publish changes to a dataset in Amazon QuickSight, a new version of the dataset is created. At any time, you can see a list of all the previous published versions of that dataset. You can also preview a specific version in that history, or even revert the dataset back to a previous version, if needed.

The following limitations apply to dataset versioning:

  • Only the most recent 1,000 versions of a dataset are shown in the publishing history, and are available for versioning.

  • After you exceed 1,000 published versions, the oldest versions are automatically removed from the publishing history, and the dataset can no longer be reverted back to them.

To revert a dataset to a previous published version, choose the Manage icon in the blue toolbar at upper right, and then choose Publishing history.

In the Publishing history pane, find the version that you want and choose Revert.

To preview the version before reverting, choose Preview.

The dataset is reverted and a confirmation message appears. The Publishing history pane also updates to show the active version of the dataset.

Once you are finished cleansing data, Select the Save & Publish button or Publish & Visualize to start creating reports.‍

Visualising Data

Here, I am going to walk you through Sales Overview Analysis. First, check out the interface.

Top Analysis Menu Bar (1) provides the following options:

  • File – Perform analysis management tasks, including creating, sharing, and publishing. Authors can use this option to make changes across all sheets or visuals in an analysis.

  • Edit – Navigate between changes that you make to the analysis. You can undo or redo changes that you make.

  • Data – Manage datasets, data fields, and parameters. Changes that you make by using this option are applied to all sheets in the analysis.

  • Insert – Use an ingress point where you can add visuals, text boxes, insights, reporting objects, filters, and parameters to an analysis. The content that you insert can be data or objects.

  • Sheets – Manage the sheet settings of the analysis, including layout settings, actions to add or remove assets from a sheet, and sheet properties.

  • Objects – Manage objects and their features, including style, canvas placement, sizing, card background, and borders. You can also manage these objects by using the Properties pane when working on a visual object.

  • Search – Access the Quick search bar. Quick search is a search bar that will begin showing results for the asset you are searching for as you type. The suggested results continue to modify as you type until you see the result that you're looking for.

Quick Access Bar (2): Located in the top-left, offering frequently used tools:

  • Undo/Redo buttons

  • Text insertion

  • Visual element controls

Data Pane (3): Opens on the left side when you click Dataset from Quick Access Bar. Here, you can,

  • Browse, search and select datafields

  • Create calculated fields

Visual Pane (4): Appears when you select the Visualize icon from Quick Access Bar. Here, you can choose various visualisation types. Some of the examples are,

  • Bar/column charts

  • KPI cards

  • Tables

  • Pivot tables

  • Pie charts

Properties Pane (5): Opens on the right side. 

  • Customise settings for selected visuals

  • Modify visual-specific properties

Sheets bar(6): Allows you to,

  • Add new sheets

  • Rename existing sheets

  • Duplicate sheets

  • Delete sheets

Let's talk about adding visuals, filters, drill-downs and various tricks and trips.

Adding Visuals:

You can create various visuals in an analysis using different datasets and visual types


After you have created a visual, you can modify it in a range of ways to customise it to your needs. Possible customisations include changing what fields map to visual elements, changing the visual type, sorting visual data, or applying a filter.
Amazon QuickSight supports up to 50 datasets in a single analysis, and up to 30 visuals in a single sheet, and a limit of 20 sheets per analysis.
You can create a visual in several ways. You can select the fields that you want and use AutoGraph to let Amazon QuickSight determine the most appropriate visual type. Or you can choose a specific visual type and choose fields to populate it. 

Adding Filters: 

You can apply filters at three different levels from the filter pane:

  • Visual-level: Affects only a single visual

  • Sheet-level: Applies to all visuals on the current sheet

  • Cross-sheet: Applies across multiple sheets in the analysis

‍Filter control can be added inside the sheet or pinned at top of sheet. There are many more settings. These can be found here.

Adding drill-downs to visual data

All visual types except pivot tables offer the ability to create a hierarchy of fields for a visual element. The hierarchy lets you drilldown to see data at different levels of the hierarchy.

Drag a field that you want to use in the drill-down hierarchy to an appropriate field well, depending on the visual type. Make sure that the label for the dragged field says Add drill-down layer. Position the dragged field above or below the existing field based on where you want it to be in the hierarchy you're creating.

Which would add an arrow to the visual (see the screenshot). Clicking on arrow will allow you to dril-down at lower levels.

Creating maps and geospatial charts

When you create a map visual, ensure your location data fields are marked as geospatial data types in the dataset. Link to Quicksight documentation.

Adjusting canvas layout

Quicksight automatically adjusts the space for the visuals on the canvas. If you need a bigger canvas space or would like to display visuals differently, you can use the Free-form option, which is located under Sheet > Layout Settings.

Publishing dashboards

When you publish an analysis, that analysis becomes a dashboard that can be shared and intracted with by users of your Amazon QuickSight account or, in some cases, with anonymous users that aren't on your account. You can choose to publish one sheet of an analysis, all sheets in the analysis, or any other combination of sheets that you want. When you publish an interactive sheet, that sheet becomes an interactive dashboard that users can interact with. When you publish a paginated report sheet, the sheet becomes a paginated report that generates and saves a snapshot of the report's data when you schedule a report in Amazon QuickSight. You can publish a dashboard that contains any combination of interactive sheets and paginated reports from the same analysis.

Use the following procedure to publish a dashboard. You can also use this procedure to rename a published dashboard. A renamed dashboard retains its security and emailed report settings.

  • Open the analysis that you want to use. Choose Publish.

  • To create a new dashboard, choose Publish new dashboard as, and then type a dashboard name.

  • To replace an existing dashboard, do one of the following. Replacing a dashboard updates it without altering security or emailed report settings.

  • To update it with your changes, choose Replace an existing dashboard and then choose a dashboard from the list.

  • To rename it, choose Replace an existing dashboard, choose a dashboard from the list, and then choose Rename. Enter a new name to rename the existing dashboard. When you rename a dashboard, it also saves any changes you made to the analysis.

Next Steps:

Amazon QuickSight offers powerful and flexible tools for data visualisation and analysis. This guide covered the essential features and interface elements to help you get started. 
Now that you're familiar with QuickSight's basic interface and features, you can begin creating your own data visualisations. Remember to experiment with different visual types and layouts to find what works best for your data storytelling needs.
Refer to the official AWS documentation for detailed guidance and best practices.

Behind every dataset lies an untold story. Ready to share yours? — Ayesha

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