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Databricks - platform, use cases

Spark/Big Datahard0.3 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

**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...

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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

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Key Concepts Tested

etllakehousesparksql

Expert Answer

59 words

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. Best practice: Use Delta; adopt medallion; leverage Workflows for orchestration; Unity Catalog for governance.

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Pro Tip

Red Flag: Treating Databricks as 'just Spark.' Pro-Move: 'We use Delta, DLT, Unity Catalog—integrated governance and lineage.'

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