Topic

#data-quality

Illustrated overview of Data Contracts for Pipeline Teams: Fail the Producer, Not Just the Test

Python🟡Intermediate

Aug 30, 202611 min read

Data Contracts for Pipeline Teams: Fail the Producer, Not Just the Test

Schema plus SLAs as a contract owned by the producer — fail the write path when the grain or freshness breaks, instead of discovering it in a warehouse test after the fact.

data-quality
python

Illustrated overview of Using LLMs Inside Data Pipelines: 4 Patterns That Actually Work in Production

AI & GenAI🟡Intermediate

Aug 30, 20268 min read

Using LLMs Inside Data Pipelines: 4 Patterns That Actually Work in Production

Beyond chatbots: how data engineers use LLMs for entity extraction, data classification, documentation generation, and pipeline triage — with cost controls.

ai
llm
python

Illustrated overview of Data Quality: Fail the Job, Open a Ticket, or Ignore Red Forever

Data Quality🟢Beginner

Aug 24, 20265 min read

Data Quality: Fail the Job, Open a Ticket, or Ignore Red Forever

Four checks — freshness, volume, schema, uniqueness — plus a written fail-versus-ticket policy, why permanently red tests train people to ignore them, and dbt vs GX vs a vendor.

data-quality
observability

Illustrated overview of Data Quality Checks in Python: Catch Bad Data Before Your Users Do

Python🟡Intermediate

Aug 16, 20269 min read

Data Quality Checks in Python: Catch Bad Data Before Your Users Do

A practical framework for adding data quality checks to Python pipelines: what to check, where to check it, and when to fail loudly vs quarantine.

python
data-quality
tutorials