Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Architectural logic: Synapse is MPP—distribution and indexing dictate load and query performance. COPY INTO is the native bulk path; ADF Copy Data supports it via PolyBase or bulk INSERT. Why COPY INTO: Reads from external stage (Blob/ADLS); parallel load across distributions;...
Red Flag: Loading via INSERT…SELECT without distribution strategy. Pro-Move: 'We use COPY INTO from staged Parquet with distribution on order_id—10TB loads in 2 hours; we tuned via DMVs.'
This medium-level Cloud/Tools question appears frequently in data engineering interviews at companies like Deloitte. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join) will help you answer variations of this question confidently.
Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.
Architectural logic: Synapse is MPP—distribution and indexing dictate load and query performance. COPY INTO is the native bulk path; ADF Copy Data supports it via PolyBase or bulk INSERT. Why COPY INTO: Reads from external stage (Blob/ADLS); parallel load across distributions; minimal log overhead. Scalability: Distribution key choice matters—hash on join keys for fact tables; round-robin for staging. CCI vs HEAP: CCI for analytics (column-store); HEAP for staging or small tables. Cost: COPY INTO is cheaper than row-by-row INSERT; staging in Blob avoids loading raw into Synapse first. Best practice: Stage Parquet in Blob → COPY INTO with FILE_TYPE=PARQUET, CREDENTIAL=Managed Identity; distribute large facts on join keys; use sys.dm_pdw_exec_requests to tune.
Red Flag: Loading via INSERT…SELECT without distribution strategy. Pro-Move: 'We use COPY INTO from staged Parquet with distribution on order_id—10TB loads in 2 hours; we tuned via DMVs.'
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According to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.