Learn how to use great_expectations, an open standard for data quality, in this beginner-level tutorial. Discover how to incorporate great_expectations into your data wrangling, exploratory data analysis, data testing, validation, and documentation processes. Explore installation, initialization, and context creation before diving into practical demonstrations using the Titanic dataset and time-series data. Gain insights on processing SparkDataFrames and using the debt_expectations extension. Master the art of eliminating pipeline debt and ensuring data quality with great_expectations.
Always Know What to Expect From Your Data With Great Expectations
Overview
Syllabus
- Video Start
- Video Content Intro
- Code & Jupyter Notebook Introduction
- great_expectations - what, why, and how?
- why you need great_expectations?
- What actually is great_expectations?
- great_expectations in simple terms
- great_expectations as the 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
- Recap
- Credits
Taught by
Prodramp
