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)
Part 1:Unleashing Data Wrangling with AWS
Part 2:Data Wrangling with AWS Tools
Part 3:AWS Data Management and Analysis
Part 4:Advanced Data Manipulation and ML Data Optimization
Part 5:Ensuring Data Lake Security and Monitoring

Data structuring

Now we have to come to the part where we need to restructure the data into a usable format from its raw format. In our use case, we extracted data in JSON format and it is good for exploratory analysis that we used a raw data format. When we move further into the data-wrangling pipeline, different file formats and structures would be more efficient.

Different file formats and when to use them

There are different file formats that are commonly used in data pipelines:

  • Readable file formats: CSV, JSON, and Extensible Markup Language (XML) are some file formats that are readable by human users:
    • CSV files are used mostly in the data extraction phase when the data needs to be shared with analysts for reading and performing further actions. The advantage is you don’t need any programming language to read the files and can be opened in the most commonly available text editors. These file formats are widely popular earlier in the data analytics community...