In the fast-paced news industry, quick reactions to real-time developments are crucial. Our client, a web publisher, operates within a complex publication structure. Having transitioned from traditional print to online publication, they manage a network of independently branded websites, fed by content from a central editorial office.

Editors require real-time insights into topics' performance, to push high-performing topics and improve those that are declining. To support editorial decisions, I conceptualized a system to alert editors and relevant staff about anomalies in traffic patterns across different categories. Additionally, technical use cases were implemented to notify about anomalies in traffic sources.

Anomaly detection relied on real-time data from an Apache Druid instance. We employed algorithms that analyzed past data and compared them with forecast estimates. When an anomaly was identified, the report was streamed into a Kafka topic. Downstream services managed push notifications to end-users.

The solution seamlessly integrated into the client's data mesh architecture. I followed an MVP approach: Product features were iteratively developed in collaboration with end-users. I designed the system for production readiness, incorporating monitoring and alerting through Grafana.

Working collaboratively, we conducted an incremental migration of an extensive JavaScript frontend to React with TypeScript, alongside parallel development of new features. The existing code lacked tests and contained undocumented but vital business logic. We embraced Test Driven Development for our React codebase, enabling stable releases.

We assisted the customer in refining the front end's logic, now backed by testing. This refinement extended to the legacy PHP backend. Through clear architecture and transparent inter-team communication, we relocated misplaced logic to intended services.

Our fully remote team included members from Germany, Hungary, and Egypt. Meanwhile, the customer organization transitioned to SAFe. As a Scrum Master, I guided the team through new processes, helping shape them and emphasizing the significance of CI/CD.

Analyzing anonymized personal movement data from supermarket cameras posed two challenges: data cleaning and behavior classification.

We calculated person movement similarity using dynamic time warping (DTW). For behavior clusters, we applied k-medoids and hierarchical agglomerative clustering on multivariate data. Using cluster centroids as classifier archetypes, our AI prototype accurately classified customer entering and exiting, validated against manual counts.

Our deliverable was a containerizable software suite assessing algorithmic strategy performance.