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How do you set up CI/CD for a PySpark ETL workflow?

Spark/Big Datahard0.7 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Microsoft
Key Concepts Tested
etloptimizationpartitionspark
Expert AnswerPremium
133 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. CI/CD for PySpark ETL involves: (1) Source control—store code in Git (GitHub/GitLab). (2) Build—use Docker or virtualenv to create reproducible environments. (3) Test—run unit tests with pytest, integration tests with small fixtures. (4) Deploy—use Databricks CLI or Terraform to deploy jobs....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Microsoft. The answer also includes follow-up discussion points that interviewers commonly explore.

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