Data Wrangling on AWS Clean and Organize Complex Data for Analysis

Book Cover
Average Rating
Published
Birmingham : Packt Publishing, Limited, 2023.
Status
Available Online

Description

Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databases Implement effective Pandas data operation with data wrangler Integrate pipelines with AWS data services Book Description 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. What you will learn Explore how to write simple to complex transformations using AWS data wrangler Use abstracted functions to extract and load data from and into AWS datastores Configure AWS Glue DataBrew for data wrangling Develop data pipelines using AWS data wrangler Integrate AWS security features into Data Wrangler using identity and access management (IAM) Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.

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Format
Edition
1st edition.
Language
English
ISBN
9781801817660, 1801817669

Notes

General Note
Description based upon print version of record.
Description
Revamp your data landscape and implement highly effective data pipelines in AWS with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Execute extract, transform, and load (ETL) tasks on data lakes, data warehouses, and databases Implement effective Pandas data operation with data wrangler Integrate pipelines with AWS data services Book Description 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. What you will learn Explore how to write simple to complex transformations using AWS data wrangler Use abstracted functions to extract and load data from and into AWS datastores Configure AWS Glue DataBrew for data wrangling Develop data pipelines using AWS data wrangler Integrate AWS security features into Data Wrangler using identity and access management (IAM) Optimize your data with AWS SageMaker Who this book is for This book is for data engineers, data scientists, and business data analysts looking to explore the capabilities, tools, and services of data wrangling on AWS for their ETL tasks. Basic knowledge of Python, Pandas, and a familiarity with AWS tools such as AWS Glue, Amazon Athena is required to get the most out of this book.
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O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

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Citations

APA Citation, 7th Edition (style guide)

Shukla, N., M, S., & Palani, S. (2023). Data Wrangling on AWS: Clean and Organize Complex Data for Analysis (1st edition.). Packt Publishing, Limited.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Shukla, Navnit, Sankar. M and Sam. Palani. 2023. Data Wrangling On AWS: Clean and Organize Complex Data for Analysis. Birmingham: Packt Publishing, Limited.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Shukla, Navnit, Sankar. M and Sam. Palani. Data Wrangling On AWS: Clean and Organize Complex Data for Analysis Birmingham: Packt Publishing, Limited, 2023.

Harvard Citation (style guide)

Shukla, N., M, S. and Palani, S. (2023). Data wrangling on AWS: clean and organize complex data for analysis. 1st edn. Birmingham: Packt Publishing, Limited.

MLA Citation, 9th Edition (style guide)

Shukla, Navnit., Sankar M, and Sam Palani. Data Wrangling On AWS: Clean and Organize Complex Data for Analysis 1st edition., Packt Publishing, Limited, 2023.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID5fc1424e-7c3f-12de-1bd6-7de091ed9c22-eng
Full titledata wrangling on aws clean and organize complex data for analysis
Authorshukla navnit
Grouping Categorybook
Last Update2025-01-24 12:33:29PM
Last Indexed2025-05-22 03:18:39AM

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