What is Great Expectations?

Great Expectations is an open-source Python library that helps you check that your data looks the way you expect it to. It lets you write “expectations” (rules) about your data and automatically validates those rules each time the data moves through a pipeline.

Let's break it down

  • Great Expectations - the name of the tool; think of it as a “data quality guard.”
  • Open-source - free to use and you can see or change the code yourself.
  • Python library - a collection of ready-made functions you can import into your Python programs.
  • Expectations - simple statements like “column A should never be empty” or “values in column B must be between 0 and 100.”
  • Validate - run the data through those statements and get a pass/fail report.
  • Pipeline - the series of steps (extract, transform, load) that moves data from source to destination.

Why does it matter?

Bad or unexpected data can cause wrong business decisions, broken software, or costly re-work. Great Expectations catches those problems early, saving time, money, and headaches by ensuring data quality before it’s used downstream.

Where is it used?

  • Finance: checking transaction records for missing fields or out-of-range amounts before they hit reporting systems.
  • E-commerce: validating product catalogs so every item has a price, SKU, and correct category.
  • Healthcare: ensuring patient data files contain required identifiers and plausible lab values before analysis.
  • Machine Learning: confirming training datasets have no NaNs or label mismatches, which helps models learn correctly.

Good things about it

  • Human-readable expectations make it easy for non-technical team members to understand data rules.
  • Integrates with many orchestration tools (Airflow, Prefect, dbt) and CI/CD pipelines.
  • Can automatically generate data documentation and data-profile reports.
  • Open-source community provides many plug-ins and examples.
  • Supports both batch and streaming data checks.

Not-so-good things

  • Initial setup and learning the expectation syntax can be steep for beginners.
  • Validation can become slow on very large datasets if not tuned properly.
  • Limited built-in support for unstructured data like free-text or images.
  • Maintaining a large suite of expectations requires ongoing effort as schemas evolve.