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Table of contents
1.
Introduction
2.
What is AWS Data Wrangler?
3.
Features of AWS Data Wrangler
3.1.
Fast Data Queries
3.2.
Data Quality Report
3.3.
Data Visualization
3.4.
Efficient Data Transformation
4.
Importing Sample Dataset
5.
Generating Quality Report in Data Wrangler
6.
Applying Filter Transform
7.
Histogram Visualization
8.
Frequently Asked Questions
8.1.
What is Amazon SageMaker?
8.2.
What is AWS S3?
8.3.
What is feature correlation?
9.
Conclusion
Last Updated: Mar 27, 2024
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AWS Data Wrangler

Author Abhinav Anand
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Collecting and preparing data is the first step for training a machine learning model. AWS Data Wrangler speeds up this tedious process from a single visual interface.

aws data wrangler

In this article, you will learn about the features and use cases of AWS Data Wrangler. You will also learn to import data into Amazon SageMaker and use Data Wrangler to perform transformations and analysis.

Let’s get started.

What is AWS Data Wrangler?

AWS Data Wrangler is a part of Amazon Sagemaker Studio. It is a visual no-code tool that allows you to select, cleanse, explore, visualize, and process your dataset. It contains over 300 built-in data transformations, which ensures you can quickly transform data without writing any code.

Let’s take a look at some features of AWS Data Wrangler.

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Features of AWS Data Wrangler

The following are some features of AWS Data Wrangler:-

Fast Data Queries

The data selection tool in AWS Data Wrangler can be used for quickly accessing and selecting tabular or image data from different data sources such as AWS S3, Amazon Redshift, Snowflake, etc.

Data Quality Report

Data Wrangler provides a data quality report that can automatically detect missing values, duplicate rows, and other anomalies such as class imbalance, outliers, etc.

Data Visualization

You can apply different preconfigured visualizations to your data, such as histograms, scatter plots, bar charts, etc. Data wrangler also supports ML-specific visualizations such as bias reports, feature correlations, and time series.

Efficient Data Transformation

Data Wrangler supports 300+ prebuilt data transformations, which allows you to transform your tabular or image dataset without writing a single line of code. You can flatten JSON files, delete duplicate rows, resize images, drop corrupted images, and more.

In the following sections, you will learn how to import the Titanic sample dataset and perform different transformations and analyses on that dataset.

Importing Sample Dataset

Following are the steps to import the Titanic sample dataset:-

Step 1: Open SageMaker Studio and click on “Import & prepare data visually”

step screenshot

Step 2: Click on “Use sample data”

step screenshot

Step 3: Wait for the import to complete

step screenshot

Now that you have imported the sample dataset, we can generate the quality report.

Generating Quality Report in Data Wrangler

The following are the steps for generating the quality report:-

Step 1: Click on the plus next to data types and then select “Add analysis”

step screenshot

Step 2: From the analysis dropdown, choose “Data Quality and Insights Report” and click on run

step screenshot

Result:

result

The image above shows a summary of the report. Detailed information can be found by scrolling below it.

In the next sections, we will transform the dataset and generate a histogram.

Applying Filter Transform

Now, we will transform the dataset by filtering based on the survived column.

Step 1: Click on the plus next to data types and then select “Add analysis”

step screenshot

Step 2: Click on “Add step”

step screenshot

Step 3: In the dropdown, select “Filter data”

step screenshot

Step 4: Click on the plus button

step screenshot

Step 5: Set “survived” as the column name, condition as “=” and set the value as 1. Click on add to perform the filtering.

step screenshot

The resulting table will only contain the records of those people who survived the crash.

Now, we will generate a histogram from the filtered table.

Histogram Visualization

Now we will generate a histogram from the transformed table.

Step 1: Click on the plus icon next to the filtered data, and then select “Add analysis”

step screenshot

Step 2: Set the analysis type to Histogram, pick sex as the x-axis value, and click on the run button.

step screenshot

Result:

result

The histogram above represents the total number of survivors from each gender.

Frequently Asked Questions

What is Amazon SageMaker?

Amazon SageMaker is a cloud-based ML platform used for building, training, and deploying machine learning models at scale. It offers a wide set of tools and services to speed up your ML workflow.

What is AWS S3?

AWS S3 stands for Simple Storage Service, which is used for storing large amounts of data such as files, images, videos, and other structured or unstructured data. It is highly scalable, which means you can store virtually unlimited amounts of data.

What is feature correlation?

Feature correlation is the statistical relationship between different attributes in a dataset. It is commonly measured with the Pearson correlation coefficient, which quantifies the linear relationship between two continuous variables from -1 to 1.

Conclusion

In summary, AWS Data Wrangler is a visual tool that speeds up the necessary data pre-processing tasks before training a machine learning model. You learned about some features of Data Wrangler and applied them to a sample data set.

You can go through the following articles to learn more about AWS:-

Happy Learning!

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