Most Companies Don't Need Real-Time Data Streaming, Engineers Argue

A growing critique in data engineering challenges the industry's default preference for real-time streaming architectures, arguing that most businesses lack the operational speed to act on millisecond-level data. Engineers often deploy complex tools like Kafka and Flink for dashboards that executives only review once a week, making the investment largely wasteful. Unlike batch processing, streaming systems are costly, difficult to debug, and demand dedicated senior teams to manage issues like out-of-order events and exactly-once semantics. Micro-batch processing — running pipelines every 15 to 60 minutes — is proposed as a practical middle ground that balances freshness with simplicity and lower cloud costs. The core advice is to default to batch processing and only adopt streaming when the business can demonstrate that data latency is directly causing measurable financial loss.