Data Wrangling on AWS: Clean and organize complex data for analysis

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.

這本實用指南將幫助您改進數據環境並在AWS上實施高效的數據管道。

購買紙質書或Kindle電子書將包含免費的PDF電子書。

主要特點：

- 在數據湖、數據倉庫和數據庫上執行提取、轉換和加載（ETL）任務
- 使用數據整理器實現有效的Pandas數據操作
- 將管道與AWS數據服務集成

書籍描述：

數據整理是將原始、混亂或非結構化數據清理、轉換和組織成結構化格式的過程。它涉及數據清理、數據集成、數據轉換和數據豐富等過程，以確保數據準確、一致且適合進行分析。《在AWS上進行數據整理》將為您提供充分利用AWS數據整理工具的知識。

首先，您將介紹在AWS上進行數據整理，並熟悉AWS中可用的數據整理服務。您將了解如何使用AWS Glue DataBrew、AWS數據整理器和AWS Sagemaker。接下來，您將探索其他AWS服務，如Amazon S3、Redshift、Athena和Quicksight。此外，您還將探索高級主題，例如使用AWS數據整理器進行Pandas數據操作、使用AWS SageMaker優化ML數據、使用Glue DataBrew構建數據倉庫，以及安全性和監控方面。

通過閱讀本書，您將具備使用AWS服務進行數據整理的能力。

學到的知識：

- 探索如何使用AWS數據整理器編寫從AWS數據存儲中提取和加載數據的簡單到複雜的轉換
- 使用抽象函數從AWS數據存儲中提取和加載數據
- 配置AWS Glue DataBrew進行數據整理
- 使用AWS數據整理器開發數據管道
- 使用身份和訪問管理（IAM）將AWS安全功能集成到數據整理器中
- 使用AWS SageMaker優化數據

適合閱讀對象：

本書適合數據工程師、數據科學家和業務數據分析師，他們希望探索在AWS上進行數據整理的能力、工具和服務，以應對他們的ETL任務。閱讀本書需要基本的Python、Pandas知識，以及對AWS工具（如AWS Glue、Amazon Athena）的熟悉，這樣才能充分利用本書的內容。

1. Introduction to Data Wrangling on AWS
2. Working with AWS GlueDataBrew
3. Introducing AWS Data Wrangler
4. Introducing Amazon SageMaker Data Wrangler
5. Working with Amazon S3
6. Working with AWS Glue
7. Working with Athena
8. Working with Quicksight
9. Perform Pandas operation with AWS Data Wrangler
10. Optimizing ML data with AWS SageMaker Data Wrangler
11. Security and Monitoring

- AWS 上的資料整理入門
- 使用 AWS Glue DataBrew
- AWS Data Wrangler 簡介
- Amazon SageMaker Data Wrangler 簡介
- 使用 Amazon S3
- 使用 AWS Glue
- 使用 Athena
- 使用 Quicksight
- 使用 AWS Data Wrangler 進行 Pandas 操作
- 使用 AWS SageMaker Data Wrangler 優化機器學習資料
- 安全性和監控