Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Databricks integrates with external storage via: (1) Cloud object storage—S3, ADLS, GCS mounted or accessed via URIs (`s3://`,...
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, spark) 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 integrates with external storage via: (1) Cloud object storage—S3, ADLS, GCS mounted or accessed via URIs (s3://, abfss://, gs://). (2) Unity Catalog—external locations and storage credentials for managed access. (3) JDBC/ODBC—for databases. (4) Kafka, Kinesis—for streaming. Example: spark.read.parquet('s3://bucket/path') or mount with dbutils.fs.mount(). Best practice: Use Unity Catalog for governance; avoid storing credentials in notebooks; use IAM roles for S3/ADLS.
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: 'Unity Catalog; IAM roles; external locations.'
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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.