AWS, Cloud Computing

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Automated Data Preparation for Amazon QuickSight Q


As businesses continue to generate vast amounts of data, managing and analyzing it effectively becomes more complex. Manual data preparation can be time-consuming and error-prone, leading to delays and inaccuracies in decision-making. To address this challenge, Amazon QuickSight Q offers automated data preparation capabilities to help businesses accelerate their data analysis and decision-making processes.

With automated data preparation, QuickSight Q simplifies importing, cleaning, and transforming data to prepare it for analysis. This blog post will explore the benefits of using automated data preparation with Amazon QuickSight Q and how it can help businesses improve their data analysis workflows.

Why use the Amazon QuickSight Q?

Automated data preparation is one of the main features of the analytical tool Amazon QuickSight. It allows users to create graphs and charts using data from multiple sources. Data preparation is essential to many advanced data analysis techniques, such as machine learning. Essentially, it allows analysts to find and fix errors in their data without interrupting the preparation process. It also makes it easy to compare two sets of data by enabling you to export one set and import another set into the same workspace. This feature has many uses, such as understanding how two systems work together or comparing two products.


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How Does Automated Data Preparation for Amazon QuickSight Q work?

QuickSight Q is mainly used in machine learning (ML) by providing self-service analytics and allowing us to query our data and get insights from given data. Primarily this data is stored in the Data Warehouse like Amazon Redshift.

Automated Data Preparation for Amazon QuickSight Q uses machine learning to information about the data and make it faster for natural language questions.

It takes a lot of time for a data analyst to prepare the data. The new automated data preparation for amazon QuickSight Q can generate new topics from analysis, reducing the time required for the data preparation and analysis. It chooses the user-friendly name for the model building, a more common word in the analysis. QuickSight selects the high value columns based on user uses for the data analysis and then creates an index of unique string values within data to process the natural language search, which is helpful for the model building process.

Automate Data Preparation for Amazon QuickSight Q Automates the choice of columns, depending on signals from existing QuickSight resources, like reports or dashboards, to help create topics relevant to the users of your enterprise. In addition to selecting high-value fields from the data set, Automated Data Preparation for Amazon QuickSight Q imports newly computed fields the authors created during their analytics, thus avoiding requiring them to recreate them in a Topic. It also updates the columns name, which is user-friendly to support a vast vocabulary relevant to the business.


Data preparation feature of QuickSight Q to preconfigure business terms for a dataset using a pre-existing dashboard, which includes subscriber information based on customer category, customer ID, and geographical location.

When the data is prepared for the dashboard building, we commonly know predefined questions and answers. Data preparation for natural language queries requires preparation for the query which are more exploratory. The same question can be asked in many ways, whereas coded queries can be asked in only one way. Amazon developed data preparation capabilities tailored for Natural Language Query to address this difference.

To do that, it uses machine learning, pre-trained, or custom models to add semantic information to datasets automatically. The results of preparing the dataset are faster for the natural language queries.


Unlike many other analytical platforms, QuickSight was cloud-native from its launch.


For an annual commitment, those who create dashboards start at $18 per month. Readers who analyze data but don’t develop the product cost 30 cents per session, with a maximum of $5 per month.

For the organizational level where the high-volume users are present, Amazon offers capacity-based pricing for QuickSight, ranging from $250 per month to over $250000 for an annual subscription.


Automated Data Preparation for AWS QuickSight Q is currently available in all the regions where the QuickSight Q is available. QuickSight is now available in the EU (Frankfurt, Ireland, and London), US West (Oregon), US East (N. Virginia and Ohio), and Asia Pacific (Mumbai, Seoul, Singapore, Sydney, and Tokyo).


Automated data preparation is an essential component of modern data analysis, and Amazon QuickSight Q makes it easier than ever for businesses to prepare their data for analysis. By using automated data preparation, companies can accelerate their time to insight, improve data quality, and reduce costs associated with manual data preparation.

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1. What is Amazon QuickSight Q?

ANS: – Amazon QuickSight Q is a cloud-powered business intelligence (BI) service that allows users to connect to various data sources, including AWS data services, third-party databases, and file formats.

2. What is automated data preparation?

ANS: – Automated data preparation uses technology to clean, transform, and prepare data for analysis automatically.

3. What are the benefits of using automated data preparation in Amazon QuickSight Q?

ANS: – Following are the benefits of using automated data preparation in Amazon QuickSight Q

  1. accelerated time to insight
  2. improved data quality
  3. Easy integration with existing data sources and tools
  4. Flexibility and customization and
  5. Cost-effectiveness.

4. How does Amazon QuickSight Q perform automated data preparation?

ANS: – Amazon QuickSight Q performs automated data preparation, from importing data, cleaning data, and transforming it to prepare it for analysis.

5. What data sources can Amazon QuickSight Q connect to?

ANS: – Amazon QuickSight Q can connect many data sources, like Amazon S3, Redshift, RDS, Aurora, and third-party databases and file formats.

WRITTEN BY Vinay Lanjewar



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