Apache Spark SQL is ideal when you need low-latency data processing and analysis or require complex analytical workflows. For instance, if you have a real-time fraud detection system that processes incoming transaction data, Spark SQL can process and analyze this data much faster than Hive's batch processing model.
Apache Hive is particularly suitable for data warehousing tasks where SQL-like query capabilities are needed over vast amounts of data stored in Hadoop. It's ideal when you have extensive historical data and require batch processing instead of low-latency queries. For example, if you're running large-scale ETL jobs that transform and aggregate terabytes of log data stored in HDFS, Hive's batch processing is efficient. In contrast, Spark SQL is more suited for interactive queries and faster in-memory processing.