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Great Expectations Quality

⬢ NIVÅ 2Tekniskt
Medel
Lönepåverkan
4 månader
Tid att lära sig
Medel
Svårighetsgrad
1
Karriärer
I korthet

Great Expectations is an open-source Python library for testing and validating data quality. Data engineers write 'expectations' (e.g., 'age column must be between 0-150') that run automatically on data pipelines. Anomalies trigger alerts. Advanced practitioners build organization-wide data quality infrastructure, catching 70-80% of issues before they reach analytics or ML. Salaries: $95-155k (USA). Mastery takes 3-4 months because it's primarily Python + SQL + domain knowledge.

Vad är Great Expectations Quality

Great Expectations is a Python library for data quality validation. Data engineers define 'expectations' (rules about data, nulls, ranges, uniqueness, regex patterns) and run them on data pipelines. When expectations fail, the system alerts engineers before bad data reaches analytics or ML models. Advanced practitioners build organization-wide data quality infrastructure: profiling datasets to auto-generate expectations, tracking expectation pass/fail rates, and integrating with orchestration tools (Airflow, Prefect) and notification systems (Slack, PagerDuty).

🔧 VERKTYG & EKOSYSTEM
Great Expectations libraryPython pandasAirflow or PrefectPandas profilerSQL validatorsJupyter notebooksData docs generatorSlack integrationsCustom validatorsPostgreSQL or Snowflake

📋 Innan du börjar

Python

💰 Lön per region

OmrådeNybörjareMidErfaren
USA$75k$115k$165k
UK£45k£70k£100k
EU€50k€78k€110k
CANADAC$80kC$125kC$180k

🎯 Karriärer som använder Great Expectations Quality

Ml Platform Engineer

❓ Vanliga frågor

What's the difference between a column expectation and a dataset expectation?
Column expectation: 'all values in age column are integers between 0-150.' Dataset expectation: 'row count doesn't exceed previous day by 50%.' Column expectations catch bad values; dataset expectations catch anomalous volume changes or data loss.
How do I integrate Great Expectations into an Airflow pipeline?
Add a GX task after data ingestion. Task runs expectations on loaded data. If expectations fail, task fails, Airflow stops and alerts (Slack, PagerDuty). Use CheckpointConfig to define which expectations run per dataset.
Can Great Expectations catch data drift?
Yes, by setting dynamic thresholds. 'Mean of metric yesterday ± 10%' becomes today's expectation. If today's data exceeds bounds, expectation fails. Requires tracking historical statistics (store them in database or config).
How many expectations should I write?
Start with 5-10 critical expectations per dataset (nullability, uniqueness, value range). Add more as you identify patterns. Target: 80% of common issues caught by first 20% of expectations (Pareto principle). Don't write hundreds; focus on high-impact ones.
What do I do when an expectation fails?
Log it (Slack, Datadog, custom webhook). Investigate root cause (upstream data issue, pipeline bug, legitimate data change). Fix root cause or adjust expectation if it's a valid new pattern. Update checkpoint to stay aligned with reality.

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