Great Expectations topics
12 short topics in Data Engineering and Analytics. Each one is a single focused exercise with a clear piece of evidence at the end.
12 topics, one exercise each.
Work through them in order, or jump to the one you need.
These are draft topic outlines (draft; instructor review pending). Full lessons follow after review; ask Ajay if you need a topic sooner.
- 01
Data expectations and checks
Inspect evidence for data expectations and checks in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 02
Schema and type validation
Inspect evidence for schema and type validation in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 03
Missing and duplicate data checks
Inspect evidence for missing and duplicate data checks in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 04
Distribution and range checks
Inspect evidence for distribution and range checks in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 05
Referential and business-rule checks
Inspect evidence for referential and business-rule checks in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 06
Dataset sampling strategy
Inspect evidence for dataset sampling strategy in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 07
Pipeline quality-gate integration
Inspect evidence for pipeline quality-gate integration in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 08
Failure evidence and reporting
Inspect evidence for failure evidence and reporting in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 09
Baseline and threshold selection
Inspect evidence for baseline and threshold selection in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 10
Drift and trend review
Inspect evidence for drift and trend review in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 11
Check versioning and ownership
Inspect evidence for check versioning and ownership in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course → - 12
Quality-check regression fixtures
Inspect evidence for quality-check regression fixtures in Great Expectations; explain the diagnosis and verify a proposed correction in a lab. Keep the exercise bounded and record the behavior supported by the chosen version.
Open micro-course →
Learn the same ideas beyond Great Expectations.
These courses share topics with this one, so you can compare how each language or tool handles the same concept.
More in Data engineering orchestration and transformation.
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