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42.7 MB
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D45D9632117217DF6582D7D55F5D7E9BE08FD98E
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April 21, 2026, 5:04 p.m.
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(Last updated: April 21, 2026, 5:06 p.m.)
| File | Size |
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| Code.zip | 4.6 KB |
| Shukla N. Data Wrangling on AWS. Clean and organize complex data...analysis 2023.pdf | 42.7 MB |
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| Uploaded by eXpOrTeRICV | Size 941.2 MB | Health [ 0 /3 ] | Added 2023-10-25 |
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NOTE
SOURCE: Shukla N. Data Wrangling on AWS. Clean and organize complex data...analysis 2023
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COVER
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MEDIAINFO
Textbook in PDF format 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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