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How would you optimize a slow-running notebook in Databricks?

Spark/Big Datahard0.5 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
Deolite
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
92 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Optimize slow Databricks notebook: (1) Cluster—right-size, enable autoscaling. (2) Data—use Delta/Parquet; filter early; avoid full scans. (3) Caching—cache reused DataFrames. (4) Parallelism—increase partitions. (5) Code—remove unnecessary cells; combine operations. (6) Use Photon if available....
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 Deolite. The answer also includes follow-up discussion points that interviewers commonly explore.

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