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 5 – exporting data

So far, we have performed several analyses on our dataset. We have also defined several feature engineering data transformations. However, it is important to remember that we have made no changes to the actual data itself yet. We have defined the data flow, which contains a series of analysis and transformation steps that can be executed before we build machine learning models. If you check the data flow, it will look something similar to the following:

Figure 10.30: Completed data flow

Figure 10.30: Completed data flow

Data Wrangler provides you with several options to export your data flow:

  • Exporting to S3: Data Wrangler gives you the ability to export your data to a location within an Amazon S3 bucket. You can do this by clicking the = button next to a data transform step and choosing Export To, and then Export to S3. Data Wrangler will create a Jupyter notebook that contains the code to do all the transformations as defined in your data flow and...