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When studying Chapter 4 of Designing Data-Intensive Applications (Encoding and Evolution), I quickly encounters a level of granularity that seems mechanical: binary formats, schema evolution, and serialization techniques. Yet behind this technical scaffolding lies something conceptually deeper. Encoding is not merely a process of serialization; it is the very grammar through which distributed systems express and interpret meaning. It is the act that allows a system’s internal thoughts — the data in memory — to be externalized into a communicable form. Without it, a database, an API, or a Kafka stream would be nothing but incomprehensible noise. ...

The intention of this page is to put together the Observability Signal Guidelines which will provide the required visibility into the systems without hurting the cost aspect of the solution. Three basic observability signals that any application emits are: Metrics, Traces and Logs The general question is - When to emit what signal? The answer lies in the intent behind the signal being emitted. What do you intend to measure with the Observability signal that you are emitting? ...

Observability is crucial for modern distributed systems, enabling engineers to monitor, debug, and optimize their applications effectively. OpenTelemetry (Otel) has emerged as a comprehensive, vendor-neutral observability framework for collecting, processing, and exporting telemetry data such as traces, metrics, and logs. This blog post will explore how custom processors in OpenTelemetry can significantly enhance your observability strategy, making it highly customizable and powerful. The repo link where I have implemented a very simple Otel-Custom-Processor. https://github.com/AkshayD110/otel-custom-processor/tree/master ...