<![CDATA[Gabardo Engineering]]>https://writing.gabardo.engineeringhttps://substackcdn.com/image/fetch/$s_!kbes!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F457be54d-f4c6-40bf-bd5a-b96250eb2e4c_675x675.pngGabardo Engineeringhttps://writing.gabardo.engineeringSubstackMon, 05 Oct 2026 08:41:06 GMT<![CDATA[Reducing 1.1 years of compute time — every single day]]>https://writing.gabardo.engineering/p/reducing-11-years-of-compute-timehttps://writing.gabardo.engineering/p/reducing-11-years-of-compute-timeFri, 22 May 2026 11:03:42 GMTAt first, leveraging a Lambda fan-out pattern for the sake of parallelisation does not seem inherently bad. But what happens when this pattern is blown out of proportion? What happens when a single Lambda call can spin up to 800 child Lambdas and those nested calls can do the same?

Imagine the architecture below - very quickly it becomes a time and mone…

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]]><![CDATA[From 8 Hours to 20 Minutes: Deploying an MVP to Production]]>https://writing.gabardo.engineering/p/from-8-hours-to-20-minutes-deployinghttps://writing.gabardo.engineering/p/from-8-hours-to-20-minutes-deployingTue, 28 Apr 2026 07:01:28 GMTThis is an engineering case study from a project I was part of around 2021–2022. At the time, I was working at a software consultancy and was placed with a large Australian tertiary education solutions provider to help take an existing system from a working MVP to something that could operate reliably in production. The system itself was already functio…

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]]><![CDATA[How a simple S3 design decision turned into a $7M cost]]>https://writing.gabardo.engineering/p/how-a-simple-s3-design-decision-turnedhttps://writing.gabardo.engineering/p/how-a-simple-s3-design-decision-turnedMon, 20 Apr 2026 07:02:01 GMTAWS S3 is widely viewed as inexpensive, effectively unbounded object storage. In this case study, that is exactly how it behaved - with the caveat that the data storage decisions our team took cost us close to 40x more than they should have, and a lifecycle policy decision nearly exploded into a $7.2 million bill whilst trying to stop the cost bleed.

The…

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]]><![CDATA[Extending the Flight Search Engine: Time, Cost, and Feasibility constraints]]>https://writing.gabardo.engineering/p/extending-the-flight-search-enginehttps://writing.gabardo.engineering/p/extending-the-flight-search-engineTue, 14 Apr 2026 23:01:53 GMTIn the previous article, we built a structural flight search engine capable of traversing an aviation graph and enumerating valid routes between airports. The system was correct — but incomplete. Pure connectivity ignores the realities that make flight search non-trivial: time, physical distance, cost, and feasibility constraints.

In this article, we ext…

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]]><![CDATA[The S3 at Scale Runbook]]>https://writing.gabardo.engineering/p/the-s3-at-scale-runbookhttps://writing.gabardo.engineering/p/the-s3-at-scale-runbookTue, 31 Mar 2026 22:01:05 GMTIn the previous article - How a simple S3 design decision turned into a $7M cost - we analysed a production system that accumulated 1.56 trillion objects in a single S3 bucket. The architecture scaled perfectly from a functional perspective — but nearly triggered a $7.2M lifecycle transition event.

The root cause was not storage volume. It was object car…

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]]><![CDATA[Oncall Nihilism]]>https://writing.gabardo.engineering/p/oncall-nihilismhttps://writing.gabardo.engineering/p/oncall-nihilismTue, 24 Mar 2026 22:01:01 GMT2:17am. The pager goes off.

An alarm fires: SQS message delay > 5 minutes.

A batch job somewhere in the system just spiked the queue from 100k messages per minute to 1 million. Your workers are auto scaling, but it takes 10–15 minutes to catch up.

Nothing is broken. The processing rate is exactly what it was five minutes ago—stable, efficient, and maxed ou…

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]]><![CDATA[Implementing a flight search engine]]>https://writing.gabardo.engineering/p/implementing-a-flight-search-enginehttps://writing.gabardo.engineering/p/implementing-a-flight-search-engineTue, 17 Mar 2026 22:00:34 GMTThe full code for Skymesh is publicly available at https://github.com/adriangabardo/skymesh

This article’s code specifically is available at https://github.com/adriangabardo/skymesh/releases/tag/v3.0.1

Overview

In previous articles in the series, we described the abstract idea of what we were trying to achieve, then laid the foundations for ingesting aviat…

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]]><![CDATA[Engineering an Aviation Graph: Data Structures and Design Decisions]]>https://writing.gabardo.engineering/p/getting-started-and-ingesting-datahttps://writing.gabardo.engineering/p/getting-started-and-ingesting-dataTue, 03 Mar 2026 22:01:10 GMTThe full code for Skymesh is publicly available at https://github.com/adriangabardo/skymesh

This article’s code specifically is available at https://github.com/adriangabardo/skymesh/releases/tag/v2.0.0

Overview

The first article in The Graph Series framed the aviation industry as a graph problem: airports as nodes, flights as edges, and routing as constrai…

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]]><![CDATA[The Aviation Industry as a Graph problem]]>https://writing.gabardo.engineering/p/the-aviation-industry-as-a-graphhttps://writing.gabardo.engineering/p/the-aviation-industry-as-a-graphTue, 17 Feb 2026 10:24:04 GMTOverview

In this article, we introduce a project that models the global aviation industry as a graph-theory problem. Airports are represented as nodes and direct flight routes as edges, forming a large, sparse, and highly non-uniform network.

The objective of this project is to explore how common aviation questions - such as route reachability, optimal pa…

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