**Architectural Logic**: Streams = CDC; Tasks = scheduled SQL; together enable near-real-time ELT. **Streams**: Capture INSERT/UPDATE/DELETE from table; query stream for changed rows. **Tasks**: Scheduled SQL; can be triggered by stream data. **Pattern**: `CREATE STREAM s ON...
This easy-level SQL question appears frequently in data engineering interviews at companies like Cognizant. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (snowflake, sql) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Architectural Logic: Streams = CDC; Tasks = scheduled SQL; together enable near-real-time ELT. Streams: Capture INSERT/UPDATE/DELETE from table; query stream for changed rows. Tasks: Scheduled SQL; can be triggered by stream data. Pattern: CREATE STREAM s ON TABLE t; CREATE TASK process SCHEDULE='1 minute' WHEN SYSTEM$STREAM_HAS_DATA('s') AS INSERT INTO target SELECT * FROM s. Use: Incremental processing; CDC pipelines. Scalability: Stream lag monitoring; task error notifications. Cost: Stream storage; task compute. Best Practice: Streams for change tracking; tasks for automation; combine for near-real-time; simpler than external CDC tools.
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Analyze My Answer β FreeAccording to DataEngPrep.tech, this is one of the most frequently asked SQL 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.