“To Kill A Monkey” we see how bad data practices kill systems” – Samuel Akinleye

Samuel Akinleye, a seasoned data engineer, has drawn a surprising but powerful parallel between the hit thriller series To Kill A Monkey by Kemi Adetiba and the world of data engineering, describing the show as “a masterclass in how pipelines collapse when built on shaky foundations.”

The eight-part series, which has become a social media sensation, is more than just gripping entertainment. According to Samuel Akinleye, its characters mirror common pitfalls in data pipeline design, from neglecting data integrity to prioritising speed over stability.

In his analysis, Efemini represents the “Overloaded Coder” skilled but resource-starved forced into desperate compromises. His partnership with Oboz, the “Flashy Scaler” obsessed with throughput over quality, mirrors engineering teams chasing vanity metrics while ignoring long-term reliability. Teacher, the “Legacy Monolith,” embodies outdated systems unable to meet modern demands.

“Through To Kill A Monkey, we see how bad data practices kill systems before they scale,” Samuel said. “Scarcity of resources, loose standards, and unchecked integrations don’t just slow you down; they poison your entire analytics foundation.”

He likened Oboz’s empty promises to “vendor-speak” that masks poor system design, stressing that robust pipelines require strict data contracts, rigorous testing, and safeguards such as circuit breakers. The show’s dramatic breakdowns, he noted, are a direct analogy for system crashes caused by schema mismatches, poor error handling, and the absence of automated alerts.

Samuel concluded that the series’ final act, in which Efemini’s daughter exposes failures publicly, is akin to end-users losing faith after repeated outages. His key takeaway: throughput should never be the ultimate goal “durable, ethical architectures that scale with integrity are what turn raw data into lasting insight.”