Data & Analytics Skill
Airflow DAG Patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and…
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Data Quality Frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts.
Production patterns for building data quality validation into your pipelines using Great Expectations, dbt tests, and versioned data contracts. It establishes checks across six quality dimensions: completeness, uniqueness, validity, accuracy, consistency, and timeliness: and fails the pipeline the moment dirty data appears, before it reaches downstream tables.
$15 one-time
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- Type Skill
- Category Data & Analytics
- Delivery Email · instant
- License One-time
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Dirty data gets stopped at the door, not reported after the damage. A bottom-up test pyramid, six quality dimensions mapped to concrete checks, and fail-fast checkpoints that halt the pipeline the moment something breaks.
- Builds the test pyramid bottom-up: schema tests first (columns exist, types match), then unit tests on single columns (not null, unique, accepted values), then integration tests across tables such as orphaned foreign keys, because upper layers are meaningless if the base fails.
- Maps all six quality dimensions to concrete checks: completeness to not-null, uniqueness to unique, validity to accepted-values and ranges, accuracy to cross-reference, consistency to business-rule expressions, timeliness to freshness windows, and reports them separately because a 95 percent overall score can hide 60 percent accuracy.
- Places fail-fast checkpoints at every pipeline stage: source validation when raw data lands, transformation validation after each step, load validation at the target. A failed checkpoint stops the pipeline, blocks downstream jobs and fires the alert channel instead of letting bad data travel.
- Replaces hardcoded thresholds with dynamic ones: row counts compared against the previous seven days with tolerance, column means against the 30-day average plus or minus two standard deviations, with seasonal profiles where the business has spikes.
- Pins the producer-consumer relationship in a versioned data contract: schema, freshness SLA, minimum quality rules and PII classification, validated automatically in CI so a breaking schema change is caught in the pull request, not in production days later.
- Runs the whole suite as one orchestrated pipeline that validates every table, generates a pass-fail report per expectation, and raises a hard failure if any table fails, so quality is a gate, not a dashboard nobody reads.
One power source. 6 lines out.
data-quality-frameworks · core
core active · 6 lines
- ✓ adding validation checkp…
Adding validation checkpoints to an ETL pipeline at source, transform, and load stages
- ✓ building a comprehensive
Building a comprehensive dbt test suite over fact and dimension tables
- ✓ establishing a versioned
Establishing a versioned data contract between a producer team and its consumers
- ✓ detecting row-count and
Detecting row-count and statistical anomalies with dynamic baselines
- ✓ wiring quality-check fai…
Wiring quality-check failures into alerting and CI/CD gates
- ✓ monitoring freshness and
Monitoring freshness and schema drift across critical tables
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Your forge
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Catch dirty data at the earliest point, before downstream cleanup costs compound
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Make better business decisions with measurable, per-dimension confidence in your data
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Prevent silent schema breakage with versioned contracts that flag breaking changes in CI
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Reduce false alarms with dynamic, history-based thresholds instead of brittle hardcoded limits
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Everything in the box.
Pick a piece up. Watch it work.
A comprehensive Great Expectations suite covering schema, keys, ranges, freshness, and statistics
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
Data engineers and analytics engineers building reliable, validated data pipelines with quality gates.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
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catch a spark: the forge will answer
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We run dbt but not Great Expectations. Can we use this without adopting a new tool?
It spans dbt tests, Great Expectations, and versioned data contracts, so a dbt-only shop can lean on the dbt test side without pulling in the rest. The six quality dimensions stay the same regardless of which tool enforces them.
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How do you validate accuracy when there is no separate source of truth to check against?
Accuracy is the hardest of the six dimensions for exactly that reason, so it leans on contracts, reconciliation rules, and reference checks rather than a magic oracle. Where no trusted reference exists, the practical guard is consistency and validity rather than absolute accuracy.
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Does it clean bad data, or only catch it?
It validates and fails the pipeline when a check breaks, so bad data is stopped rather than quietly repaired. Fixing the underlying records, or the upstream system producing them, is your job once the gate flags it.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.