Data Freshness as a Reliability Signal: A New Way Of Creating Batch Systems

#batch #pipeline #pipelines #manufacturing #monitoring #service #data #failure #impact #SRE #global

Did you know that in physical manufacturing, a batch system (or batch production) is a method where identical products move through each manufacturing stage together before the entire group advances to the next step. These systems can be both large and complex. When you are either in charge of such a system or your livelyhood depends on one, you really want to make sure that its always up and running.

Many of today's modern enterprise workflow still depend on scheduled batch jobs, file transfers, and data pipelines. Failures in such systems are often identified only after downstream business impact happens. Let us agree that that's not good for anyone!

In this seminar we'll be revealing the Data Freshness Reliability (DFR) framework for monitoring delivery-time reliability in batch-oriented systems. Instead of relying only on scheduler success or failure, this new technique evaluates whether expected data arrives within a defined time window. If something goes wrong, now you'll know about it sooner and will be able to do something about it before things get out of hand.

The framework introduces freshness Service Level Indicators (SLIs), Service Level Objectives (SLOs), burn rate calculations, and classification of delivery events as fresh, late, or missing. A reference architecture together with a synthetic evaluation is used for validating the approach.

Attend this seminar and you can find out how freshness-based monitoring can expose delivery problems which may not be visible in traditional job-centric monitoring systems.

If your job currently or in the future may depend on a batch system, then this is the seminar that you must come to!

We'll see you there!