Data Quality Check you own this product

prerequisites
Basic understanding of data quality concepts and metrics • Familiarity with AWS services and console, including AWS Glue and AWS CloudWatch • Knowledge of Python and the PyDeequ library for data quality checks • Understanding of how to use Jupyter notebooks with AWS Glue Studio, AWS Glue job development and deployment processes • Familiarity with data quality observability and monitoring tools.
skills learned
Implementing Data Quality Jobs on AWS • Automating infrastructure using CloudFormation • Developing Data Quality jobs using PyDeequ and AWS Glue • Observing and monitoring Data Quality jobs.
Gianluigi Mucciolo
1 week · 6-8 hours per week · INTERMEDIATE
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Nextstellar Corp has recently migrated to the cloud, and for the first time, they can analyze 100% of their company’s data. But there’s a problem: your CEO isn’t confident in your data’s quality. He wants to add more data sources and collect more user behavior information, and ensure these new sources are top-notch by utilizing the Python- (or Scala-) based Deequ library. Your task is to utilize Jupyter Notebooks with AWS Glue Studio to experiment with PyDeequ for data quality assessment. Next, you’ll enhance Nextstellar’s assessment capabilities by employing AWS Glue jobs to react and take action on data quality issues. Finally, you’ll monitor data quality using CloudWatch for prompt response and maintenance of data reliability.

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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