Amazon QuickSight is a fast, cloud-powered business intelligence service that makes it easy to deliver insights to everyone in your organization.

As a fully managed service, QuickSight lets you easily create and publish interactive dashboards that include ML Insights. Dashboards can then be accessed from any device, and embedded into your applications, portals, and websites.

With our Pay-per-Session pricing, QuickSight allows you to give everyone access to the data they need, while only paying for what you use.

Using Amazon QuickSight, you can do the following:

1. Get started quickly – Sign in, choose a data source, and create your first visualization in minutes.

2. Access data from multiple sources – Upload files, connect to AWS data sources, or use your own external data sources.

3. Take advantage of dynamic visualizations – Smart visualizations are dynamically created based on the fields that you select.

4. Get answers fast – Generate fast, interactive visualizations on large data sets.

5. Tell a story with your data – Create data dashboards and point-in-time visuals, share insights and collaborate with others.

Delivers responsive performance by using a robust in-memory engine (SPICE).

Amazon QuickSight offers Standard and Enterprise editions.

You can explore Amazon QuickSight using some sample data.

There are also a variety of data sets available free online that you can use with Amazon QuickSigh.

You can use a variety of sources for data analysis, including files, AWS services, and on-premises databases.

To get ready to create analyses, you create data sets based on your data sources.

A data set identifies the specific fields and rows that you want to use.

In addition to raw data, a data set stores any changes you make, so it's ready the next time you want to analyze the data.

You can create multiple analyses using the same data set.

You can also use multiple data sets in a single analysis.

SPICE is Amazon QuickSight's Super-fast, Parallel, In-memory Calculation Engine. SPICE is engineered to rapidly perform advanced calculations and serve data.

Pay only for what you use: QuickSight's pay-per-session pricing means you only pay when your users access the dashboards or reports.

Scale to all your users: With its serverless architecture, QuickSight scales automatically from tens of users to tens of thousands without any infrastructure management, capacity planning or scripting.

Embed analytics in apps: Enhance your applications with embedded QuickSight dashboards, speeding up time to market and saving on development costs. Empower users with interactive filtering and drill downs in QuickSight dashboards; iterate faster with easy updates via QuickSight UI or APIs.

Data preparation is the process of transforming raw data for use in an analysis. This includes making changes like the following:

1. Filtering out data so you can focus on what's important to you

2. Renaming fields to make them easier to read

3. Changing data types so they are more useful

4. Adding calculated fields to enhance analysis

5. Creating SQL queries to refine data

Visuals: A visual, also known as a data visualization, is a graphical representation of a data set using a type of diagram, chart, graph, or table.

Insights: If you aren't certain what to look for in a data set, you can use a suggested insight to quickly create a visual. Suggested insights, officially called ML Insights, propose potentially useful visuals based on a evaluation of your data.

Sheets: A sheet is a set of visuals that are viewed together in a single page.

Scene: is a representation of an analysis at a given point in time, or with specific settings. It shows the visuals that are on the analysis at that time, but the data in those visuals continues to update. It is not a static snapshot. You capture a scene for use in a story.

Stories: A story is a set of one or more scenes (captured visuals) that you can play like a slideshow. You can use these to step through different iterations of an analysis.

Dashboards: A dashboard is a read-only snapshot of an analysis that you can share with other Amazon QuickSight users for reporting purposes.

The first time you create an analysis, the typical workflow looks like this:

1. Add or upload a data source, and use it to create a new data set.

2. (Optional) Prepare the data – get it ready for reports by standardizing field names, or adding calculations, for example.

3. Visualize (create) a new analysis from the data set.

4. Choose some fields to create the first visual in the analysis. You can use AutoGraph to dynamically create a visual based on the number and type of fields you choose. Alternatively, you can choose the visual type you want to use.

5. (Optional) Make changes to the visual if you want to (for example, by adding a filter or changing the visual type).

6. (Optional) Add more visuals to the analysis. You can resize and arrange them in the workspace.

7. (Optional) Capture the analysis into a story to create a narrative about some aspect of the data analysis.

8. (Optional) Publish the analysis as a dashboard to share insights with other users.

Quicksight - Creating One Chart