Serverless Transformation you own this product

prerequisites
Basic understanding of data storage and management concepts • Knowledge of Python programming language and PySpark and AWS Glue libraries • Understanding of ETL workflows and transformations, including AWS Glue Jupyter Notebook usage • Basic knowledge of CI/CD concepts and workflows • Familiarity with Git and version control systems, code refactoring, and best practices • Understanding of unit testing and Python testing frameworks
skills learned
Transforming standardized data into curated data using PySpark • Automating infrastructure using CloudFormation • Developing ETL jobs using AWS Glue Jupyter Notebook • Implementing CI/CD for code release in AWS Glue Jobs • Refactoring Jupyter code and deploying through the CI/CD pipeline
Gianluigi Mucciolo
1 week · 6-8 hours per week · INTERMEDIATE
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Nextstellar Corp is very excited—they’re taking all their data to the cloud! They’ve recently migrated and have an early-defined data lake solution. The CEO has approached you to deliver the next step of their cloud data process: using AWS Glue to apply the transformation logic and store the curated data in Amazon S3. You’ll utilize Jupyter Notebooks to curate and standardize your data sets—crafting, organizing, and managing datasets to ensure they are easily accessible and usable—then design a CI/CD pipeline to test and deploy code with a single push after completion.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Gianluigi Mucciolo

prerequisites

This liveProject is for engineers who want to build a data lake lambda architecture using AWS fully managed services. You will need to know:


TOOLS
  • Basics of the Linux console
  • Basics of AWS
  • Basics of the AWS console
  • Basics of the AWS CLI

TECHNIQUES
  • Basics of infrastructure automation
  • Basics of big data Lambda architecture

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