Batch processes bounded datasets in discrete runs; streaming processes unbounded data with continuous execution. **Why the distinction matters**: Batch has predictable cost (run N times, pay N × job cost); streaming has always-on cost and state management. **Data Fusion context**: Batch templates (e.g., JDBC to BigQuery) are scheduled; streaming uses Pub/Sub or Kafka sources....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Aarete, Freecharge. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.