Preface

Welcome to the world of Data Wrangling on AWS! In this comprehensive book, we will explore the exciting field of data wrangling and uncover the immense potential of leveraging Amazon Web Services (AWS) for efficient and effective data manipulation and preparation. Whether you are a data professional, a data scientist, or someone interested in harnessing the power of data, this book will provide you with the knowledge and tools to excel in the realm of data wrangling on the AWS platform.

Data wrangling, also known as data preparation or data munging, is a critical step in the data analysis process. It involves transforming raw data into a clean, structured format that is ready for analysis. With the exponential growth of data and the increasing need for data-driven decision-making, mastering the art of data wrangling has become essential for extracting valuable insights from vast and complex datasets.

In this book, we will guide you through a series of chapters, each focusing on a specific aspect of data wrangling on AWS. We will explore various AWS services and tools that empower you to efficiently manipulate, transform, and prepare your data for analysis. From AWS Glue and Athena to SageMaker Data Wrangler and QuickSight, we will delve into the powerful capabilities of these services and uncover their potential for unlocking valuable insights from your data.

Throughout the chapters, you will learn how to leverage AWS’s cloud infrastructure and robust data processing capabilities to streamline your data-wrangling workflows. You will discover practical techniques, best practices, and hands-on examples that will equip you with the skills to tackle real-world data challenges and extract meaningful information from your datasets.

So, whether you are just starting your journey in data wrangling or looking to expand your knowledge in the AWS ecosystem, this book is your comprehensive guide to mastering data wrangling on AWS. Get ready to unlock the power of data and unleash its full potential with the help of AWS’s cutting-edge technologies and tools.

Let’s dive in and embark on an exciting journey into the world of data wrangling on AWS!

Who this book is for

Data Wrangling on AWS is designed for a wide range of individuals who are interested in mastering the art of data wrangling and leveraging the power of AWS for efficient and effective data manipulation and preparation. The book caters to the following audience:

• Data Professionals: Data engineers, data analysts, and data scientists who work with large and complex datasets and want to enhance their data-wrangling skills on the AWS platform
• AWS Users: Individuals who are already familiar with AWS and want to explore the specific services and tools available for data wrangling
• Business Analysts: Professionals involved in data-driven decision-making and analysis who need to acquire data-wrangling skills to derive valuable insights from their data
• IT Professionals: Technology enthusiasts and IT practitioners who want to expand their knowledge of data wrangling on the AWS platform

While prior experience with data wrangling or AWS is beneficial, the book provides a solid foundation for beginners and gradually progresses to more advanced topics. Familiarity with basic programming concepts and SQL would be advantageous but is not mandatory. The book combines theoretical explanations with practical examples and hands-on exercises, making it accessible to individuals with different backgrounds and skill levels.

What this book covers

Chapter 1, Getting Started with Data Wrangling: In the opening chapter, you will embark on a journey into the world of data wrangling and discover the power of leveraging AWS for efficient and effective data manipulation and preparation. This chapter serves as a solid foundation, providing you with an overview of the key concepts and tools you’ll encounter throughout the book.

Chapter 2, Introduction to AWS Glue DataBrew: In this chapter, you will discover the powerful capabilities of AWS Glue DataBrew for data wrangling and data preparation tasks. This chapter will guide you through the process of leveraging AWS Glue DataBrew to cleanse, transform, and enrich your data, ensuring its quality and usability for further analysis.

Chapter 3, Introducing AWS SDK for pandas: In this chapter, you will be introduced to the versatile capabilities of AWS Data Wrangler for data wrangling tasks on the AWS platform. This chapter will provide you with a comprehensive understanding of AWS Data Wrangler and how it can empower you to efficiently manipulate and prepare your data for analysis.

Chapter 4, Introduction to SageMaker Data Wrangler: In this chapter, you will discover the capabilities of Amazon SageMaker Data Wrangler for data wrangling tasks within the Amazon SageMaker ecosystem. This chapter will equip you with the knowledge and skills to leverage Amazon SageMaker Data Wrangler’s powerful features to efficiently preprocess and prepare your data for machine learning projects.

Chapter 5, Working with Amazon S3: In this chapter, you will delve into the world of Amazon Simple Storage Service (S3) and explore its vast potential for storing, organizing, and accessing your data. This chapter will provide you with a comprehensive understanding of Amazon S3 and how it can be leveraged for effective data management and manipulation.

Chapter 6, Working with AWS Glue: In this chapter, you will dive into the powerful capabilities of AWS Glue, a fully managed extract, transform, and load (ETL) service provided by AWS. This chapter will guide you through the process of leveraging AWS Glue to automate and streamline your data preparation and transformation workflows.

Chapter 7, Working with Athena: In this chapter, you will explore the powerful capabilities of Amazon Athena, a serverless query service that enables you to analyze data directly in Amazon S3 using standard SQL queries. This chapter will guide you through the process of leveraging Amazon Athena to unlock valuable insights from your data, without the need for complex data processing infrastructure.

Chapter 8, Working with QuickSight: In this chapter, you will discover the power of Amazon QuickSight, a fast, cloud-powered business intelligence (BI) service provided by AWS. This chapter will guide you through the process of leveraging QuickSight to create interactive dashboards and visualizations, enabling you to gain valuable insights from your data.

Chapter 9, Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas: In this chapter, you will explore the powerful combination of AWS Data Wrangler and pandas, a popular Python library for data manipulation and analysis. This chapter will guide you through the process of leveraging pandas operations within AWS Data Wrangler to perform advanced data transformations and analysis on your datasets.

Chapter 10, Data Processing for Machine Learning with SageMaker Data Wrangler: In this chapter, you will delve into the world of machine learning (ML) data optimization using the powerful capabilities of AWS SageMaker Data Wrangler. This chapter will guide you through the process of leveraging SageMaker Data Wrangler to preprocess and prepare your data for ML projects, maximizing the performance and accuracy of your ML models.

Chapter 11, Data Lake Security and Monitoring: In this chapter, you will be introduced to Identity and Access Management (IAM) on AWS and how closely Data Wrangler integrates with AWS’ security features. We will show how you can interact directly with Amazon Cloudwatch logs, query against logs, and return the logs as a data frame.

To get the most out of this book

To get the most out of this book, it is helpful to have a basic understanding of data concepts and familiarity with data manipulation techniques. Some prior exposure to programming languages, such as Python or SQL, will also be beneficial, as we will be utilizing these languages for data-wrangling tasks. Additionally, a foundational understanding of cloud computing and AWS will aid in grasping the concepts and tools discussed throughout the book. While not essential, having hands-on experience with AWS services such as Amazon S3 and AWS Glue will further enhance your learning experience. By having these prerequisites in place, you will be able to fully engage with the content and successfully apply the techniques and practices covered in Data Wrangling on AWS.

Software/hardware covered in the book	Operating system requirements
AWS	Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Data-Wrangling-on-AWS. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

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.”

Tips or important notes

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