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Databricks - platform, use cases

Spark/Big Datahard0.3 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
NAB
Key Concepts Tested
etllakehousesparksql
Expert AnswerPremium
59 wordsInterview-ready
**Why Databricks matters**: Unified lakehouse platform—reduces tool sprawl, accelerates delivery. **Platform**: Spark-based; Delta Lake; ML Runtime; notebooks; Workflows; Unity Catalog. **Use cases**: ETL pipelines, ML training/inference, BI/SQL, streaming. **Scalability trade-offs**: Managed = less control, more convenience. **Cost implications**: DBU-based; optimize with job clusters, Photon, autoscaling....
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 NAB. The answer also includes follow-up discussion points that interviewers commonly explore.

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