Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**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...
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.
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 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.
Red Flag: Credentials in notebooks. Pro-Move: 'Secrets Scope; CMK for compliance; audit logging.'
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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 an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.