Validate Data with Great Expectations
Enforce schema and distribution rules on input data.
Validate Data with Great Expectations is a free MLOps Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Validate Data as It Arrives
Bad input quietly poisons predictions. Great Expectations checks incoming data against rules before it ever reaches your model. 🛡️
An Expectation Is a Rule
An expectation is one declarative claim about your data, like a column has no nulls or values fall in a range.
expect_column_values_to_not_be_null("user_id")Expectations Read Like English
The method names spell out intent, so anyone can review them. A clear declarative style makes data contracts easy to audit.
expect_column_values_to_be_between(
"age", 0, 120)Group Rules in a Suite
Related expectations live together. An Expectation Suite is the full set of rules you run against one dataset.
Profile to Bootstrap Rules
You need not write every rule by hand. A profiler scans sample data and suggests a starter suite you then refine.
Run a Validation
Pointing a suite at new data produces a pass or fail result. Each validation reports exactly which expectations broke and how.
Check Schema, Not Just Values
Columns get renamed and reordered without warning. A schema expectation locks the column set and order in place.
expect_table_columns_to_match_ordered_list(cols)Catch Distribution Shift
Values can stay legal yet drift in shape. A distribution expectation flags when the mean or spread strays from training.
Data Docs You Can Share
Great Expectations renders results as a web report. These Data Docs give your whole team a readable data quality view.
Wire It Into the Pipeline
Validate at ingestion so bad data stops early. A failed suite can halt the pipeline before training or serving ever starts.
A Living Data Contract
Your suite is a shared agreement on what good data looks like. Treat the contract as code: review it and version it in Git.
Quick Check
What is the right name for the full set of rules you run against one dataset?
Recap: Enforce a Data Contract
You now use Great Expectations: declarative suites for nulls, ranges, schema, and distribution that validate data and stop bad input early. ✅
Frequently asked questions
Is the “Validate Data with Great Expectations” lesson free?
Yes — the full text of “Validate Data with Great Expectations” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Validate Data with Great Expectations”?
Enforce schema and distribution rules on input data. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Validate Data with Great Expectations” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Unit Test Your Data Pipeline
- Behavioral Tests for Models
- Set Quality Gates and Thresholds
- Validate Data with Great Expectations