Batch ETL vs Streaming ETL: which is better for near-real-time analytics?

Streaming ETL enables near-real-time analytics by processing data as it arrives, making it optimal for use cases with minimal delay—such as monitoring, fraud detection, user behaviour, or IoT analytics. Batch ETL processes data in scheduled jobs, making it better suited to nightly loads or scenarios where latency is less critical. For near-real-time analytics where speed matters, streaming ETL is preferred, though batch can suffice if freshness isn’t crucial.