Writing archive

Case Studies

Each case study is a problem I worked on, written up as the frame, the approach, and the trade-offs. None of them is a product pitch — the point is the reasoning, including the parts that turned out to be expensive.

The same threads run through all of them: getting data out of databases and into the shape downstream consumers need, moving services onto shared infrastructure instead of letting each one build its own path, and keeping that infrastructure dependable enough for AI initiatives to be built on top of it.

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    Case study

    Binary data delivery for retail measurement

    A case study about querying retail measurement data out of databases and delivering it as binary payloads through a delivery pipeline — filters, enrichments, and file handling at scale.

    Case study

    Foundations for AI-driven initiatives

    A case study about engineering the data and delivery foundations that AI-driven initiatives depend on — reusing the pipeline, not rebuilding bespoke paths.