Great Expectations Quality

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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.

Great Expectations Quality이란

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).

🔧 도구 & 생태계

Great Expectations libraryPython pandasAirflow or PrefectPandas profilerSQL validatorsJupyter notebooksData docs generatorSlack integrationsCustom validatorsPostgreSQL or Snowflake

📋 시작하기 전에

Python

💰 지역별 연봉

지역	주니어	미들	시니어
USA	$75k	$115k	$165k
UK	$45k	$70k	$100k
EU	$50k	$78k	$110k
CANADA	$80k	$125k	$180k

🎓 자격증

Great Expectations Official Documentation DataCamp, Data Quality & Testing Udemy, Data Quality with Great Expectations

🎯 Great Expectations Quality을(를) 활용하는 직무

Ml Platform Engineer

❓ 자주 묻는 질문

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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