Always know what to expect from your data with great_expectations

Great Expectations is a shared, open standard for data quality. It helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.

In this beginner-level great_expectations tutorial, my objective is to help you learn more about great_expectations and find a way to incorporate great_expectations into your own data wrangling or exploratory data analysis as a data test or data validation, or data documentation tool.

Tutorial Level: Beginners or Starters

Content Timeline:

• Code & Jupyter Notebook Introduction
• great_expectations — what, why, and how?
• Why do you need great_expectations?
• What actually is great_expectations?
• great_expectations in simple terms
• great_expectations as a data documentation tool
• great_expectations method at a glance
• great_expectations installation
• great_expectations initialization
• great_expectations context
• great_expectations demo with the titanic dataset
• Export and apply great_expectations config
• Working with time-series dataset
• Processing SparkDataFrame
• great_expectations extension — debt_expectations

Library great_expectations GitHub:

GitHub - great-expectations/great_expectations: Always know what to expect from your data.

GitHub URL for the samples in the Video:

publiccode/python/greatexpectation-work at master · prodramp/publiccode

Be Good and Do Good.

Thanks

Avkash Chauhan