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Quick Analysis With AWS QuickSight

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Quick Analysis With AWS QuickSight

QuickSight empowers data analysts to build intuitive reports in just a few minutes without any significant set up by IT.

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AWS QuickSight is a Business Intelligence (BI) application that can help build interactive visualizations on top of various data sources that are hosted on Amazon cloud infrastructure. In this post, I will show you how easy it is to build your first analysis in under 10 minutes, all in the AWS cloud infrastructure.

Navigating With QuickSight

Once you have registered and started QuickSight, you will see the homepage.

Key navigation icons as noted in the diagram below:

  • The QuickSight icon on the top left is a quick way to return to the homepage.
  • To upload new data, click Manage Data on the top right.
  • To create a new analysis, click New Analysis on the left side below the QuickSight logo.

To manage your account settings, click on the person icon in the top right corner.

Screen Shot 2017-02-02 at 3.07.06 PM.pngFigure 1: QuickSight navigation.

Loading Data to QuickSight

For this demo, I have used College Scorecard data from Data.gov and also saved the file to this GitHub location.

Let’s explore the steps to load our data to QuickSight.

  1. From the QuickSight homepage, click the Manage data icon.
  2. Click New Data Set and you will see the “Create a Data Set” page with several options as shown in the figure below. Select the Upload a file option and upload MERGED2013_PP.csv from your local desktop to AWS QuickSight.Screen Shot 2017-02-02 at 3.20.11 PM.png
  3. After you have successfully uploaded the MERGED2013_PP.csv file, you will see a confirmation screen from QuickSight as shown in the figure below. Click Next to accept the defaults.
  4. After the confirmation page, QuickSight imports the data to SPICE and provides quick access to visualize the data (as shown in the figure below). Click Visualize and proceed to the next section.

Screen Shot 2017-02-02 at 3.45.40 PM.png

Starting Your Visualizations

Now, you are ready to start visualizing data using the built-in charts in QuickSight. Let’s see how to create our first useful analysis, which is also demonstrated in the figure. Follow the steps below to create a chart showing the average tuition fees by state:

  • First, select the Horizontal Bar Chart from Visual Type.
  • Next, select STABBR as the Y-axis and TUITFTE as the value field.
  • Next, in the Fields wells section, change the aggregation type of the value from the default Sum to Average.

The visualization is complete as shown in the figure below. Now, you can explore the chart and get more insights from the data.

Screen Shot 2017-02-02 at 3.49.48 PM.pngBuilding Multiple Visualizations

You can then add another visual for the same dataset pretty easily and get a further understanding of the data. In the steps below, we will see how to build a pie chart that shows the sum of in-state tuition by city.

  • First, click + to add a new visual.
  • Select the Pie Chart from the Visual Type.
  • Next, select City as the Group/Color and TUITIONFEE_IN as the value field.
  • Notice the default aggregation for value is automatically set to Sum.

The visualization is complete, as shown in the figure below. Now, you can explore the chart and get more insights from the data.

Screen Shot 2017-02-02 at 3.51.09 PM.png

Summary

Amazon QuickSight is an innovative and next generation cloud-hosted BI platform that addresses shortfalls of traditional BI systems. QuickSight can source data from various sources including relational databases, files, streaming, and NoSQL databases. QuickSight also comes with an in-memory caching layer that can cache and calculate aggregates on the fly. With QuickSight, data analysts are truly empowered and can build intuitive reports in minutes without any significant set up by IT. In the next article, we will look into details on onboarding various data sources that are supported by QuickSight.

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Topics:
aws ,big data ,quicksight ,business intelligence ,analysis

Published at DZone with permission of Rajesh Nadipalli, DZone MVB. See the original article here.

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