Connecting technical metrics to business goals
It’s no longer enough to worry about whether something is “up and running.” We need to understand whether it’s running with sufficient performance to meet business requirements. Traditional observability tools that track latency and throughput are table stakes. They don’t tell you if your data is current, or whether streaming data is arriving in time to feed an AI model that’s making real-time decisions. True visibility requires tracking the flow of data through the system, ensuring that events are processed in order, that consumers keep up with producers, and that data quality is consistently maintained throughout the pipeline.
Streaming platforms should play a central role in observability architectures. When you’re processing millions of events per second, you need deep instrumentation at the stream processing layer itself. The lag between when data is produced and when it is consumed should be treated as a critical business metric, not just an operational one. If your consumers fall behind, your AI models will make decisions based on old data.
The schema management problem
Another common mistake is treating schema management as an afterthought. Teams hard-code data schemas in producers and consumers, which works fine initially but breaks down as soon as you add a new field. If producers emit events with a new schema and consumers aren’t ready, everything grinds to a halt.
