Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Databricks Git integration: (1) Repos—clone Git repos into workspace; sync and push changes. (2) Repos in Git—notebooks live in repos,...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like PWC. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Databricks Git integration: (1) Repos—clone Git repos into workspace; sync and push changes. (2) Repos in Git—notebooks live in repos, not workspace. Configure: Settings > Git Integration; add personal access token. Workflow: Clone repo, edit notebook, commit, push. For CI/CD: Use Databricks CLI or API to deploy from Git. Best practice: Use feature branches; enable branch protection; integrate with GitHub Actions/GitLab CI; avoid storing secrets in repos.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Secrets in repos. Pro-Move: 'Feature branches; Repos; CI from Git.'
Some links below are affiliate links. If you buy through them we may earn a small commission at no extra cost to you — it helps keep DataEngPrep free.
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.