Connecting technical metrics to enterprise targets
It’s now not sufficient to fret about whether or not one thing is “up and working.” We have to perceive whether or not it’s working with adequate efficiency to satisfy enterprise necessities. Conventional observability instruments that observe latency and throughput are desk stakes. They don’t inform you in case your knowledge is present, or whether or not streaming knowledge is arriving in time to feed an AI mannequin that’s making real-time selections. True visibility requires monitoring the movement of knowledge by way of the system, making certain that occasions are processed so as, that customers sustain with producers, and that knowledge high quality is constantly maintained all through the pipeline.
Streaming platforms ought to play a central position in observability architectures. Once you’re processing hundreds of thousands of occasions per second, you want deep instrumentation on the stream processing layer itself. The lag between when knowledge is produced and when it’s consumed ought to be handled as a essential enterprise metric, not simply an operational one. In case your shoppers fall behind, your AI fashions will make selections based mostly on outdated knowledge.
The schema administration drawback
One other widespread mistake is treating schema administration as an afterthought. Groups hard-code knowledge schemas in producers and shoppers, which works nice initially however breaks down as quickly as you add a brand new discipline. If producers emit occasions with a brand new schema and shoppers aren’t prepared, all the things grinds to a halt.
