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Data Wrangling on AWS

Data Wrangling on AWS

By : Shukla, Sankar M, Sam Palani
4.9 (7)
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Data Wrangling on AWS

Data Wrangling on AWS

4.9 (7)
By: Shukla, Sankar M, Sam Palani

Overview of this book

Data wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.
Table of Contents (19 chapters)
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1
Part 1:Unleashing Data Wrangling with AWS
3
Part 2:Data Wrangling with AWS Tools
7
Part 3:AWS Data Management and Analysis
12
Part 4:Advanced Data Manipulation and ML Data Optimization
15
Part 5:Ensuring Data Lake Security and Monitoring

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

import sysimport awswrangler as wr
print(wr.__version__)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe' WITH SERDEPROPERTIES (    "input.regex" = "^([\\w\\s.+-]{11})\\s([\\w\\s.+-]{8})\\s([\\
    w\\s.+-]{9})\\s([\\w]{4})\\s([\\d]{4})\\s([\\d]{4})")
LOCATION 's3://<<location your file/'

Any command-line input or output is written as follows:

git clone https://github.com/aws-samples/aws-database-migrationsamples.gitcd aws-database-migration-samples/mysql/sampledb/v1/

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Click on the Upload button.”

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