Book Image

Data Wrangling on AWS

By : Navnit Shukla, Sankar M, Sampat Palani
5 (1)
Book Image

Data Wrangling on AWS

5 (1)
By: Navnit Shukla, Sankar M, Sampat 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)
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

Step 1 – logging in to SageMaker Studio

In this section, we will cover the steps to log in and navigate inside the AWS console and SageMaker. If you are already familiar with using SageMaker, you can skip this section and move on directly to the next one.

After you have created your account and set up a SageMaker Studio domain and created a user, as covered in Chapter 4, you can log in to the AWS console and choose SageMaker. You can either navigate to SageMaker in the All Services section under Machine Learning or start typing SageMaker in the search box at the top of the AWS console.

Figure 10.1: AWS console – SageMaker

Figure 10.1: AWS console – SageMaker

Once you are on the SageMaker screen, you should see the domain you created in the prerequisite section in Chapter 4. Make sure that the status of the domain is InService before proceeding. If you do not see a domain at all, verify to make sure you are in the same region where you created your domain. Check and switch...