DNS Log Analytics for Operational Signals
DNS and web logs are often treated as infrastructure exhaust. This project looked at them as a data-science source: a stream of behavioral signals that can reveal traffic patterns, unusual activity, service demand, and early operational stress.
Analytics framing
The work focused on how request logs can be transformed into structured features for analysis. Useful signals include query frequency, repeated domains, traffic bursts, time-of-day patterns, unusual source behavior, and changes in service demand.
Why it matters
Log analytics can support more than security monitoring. It can help teams understand how systems are actually used, detect disruptions earlier, and connect technical observability with planning questions such as capacity, customer behavior, and service reliability.
Portfolio relevance
This project sits at the intersection of analytics engineering, observability, and data science. It shows how low-level operational data can become a decision-support asset when it is cleaned, modeled, and interpreted carefully.