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Home/Questions/Spark/Big Data/Explain data encryption in Databricks, both at rest and in transit.

Explain data encryption in Databricks, both at rest and in transit.

Spark/Big Datahard0.6 min readPremium

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Databricks encryption: At rest—data in Databricks-managed S3/ADLS and Delta tables is encrypted via cloud provider (AWS KMS, Azure Key...

🤖 Analyze Your Answer
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
TCS
Key Concepts Tested
optimizationpartition

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like TCS. 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.

How to Approach This

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.

Expert Answer
113 words

Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Databricks encryption: At rest—data in Databricks-managed S3/ADLS and Delta tables is encrypted via cloud provider (AWS KMS, Azure Key Vault); customer-managed keys (CMK) optional. In transit—TLS 1.2+ for all connections (REST, JDBC, internal). Table ACLs and secrets management via Databricks Secrets. Best practices: enable encryption for workspace and metastore; use Unity Catalog for centralized governance; store credentials in Databricks Secrets (not in notebooks); enable audit logging; use private link/VPC peering for sensitive environments.

Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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