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Data pipelines

Take in data as it arrives, reject what is clearly wrong, and show the latest good value without slowing down your app. Under the hood: Python, AWS Kinesis, Lambda and Redis.

Vendors post bursts. Spreadsheets arrive late. A heart rate of 0 and a duplicated row should not land next to the real ones.

I put Kinesis or a queue in front, Python rules on the sample, and a small read model for the screen that asks “what is the latest.”

The raw feed stays. The chart does not read it on every page load.

What this includes

  • •Ingest that survives a burst without blocking the web app.
  • •Rules that drop impossible or duplicate values.
  • •A read API for the latest accepted record.
  • •A way to replay a bad day without rewriting history by hand.

What you leave with

  • •A screen that shows the last good reading, not the last packet.
  • •Imports that can be run twice.
  • •A trail of what was rejected and why.

How it starts

We take one feed. I write the accept rules from your real bad samples, then the read path.

When to use it

Use this when data arrives in bursts, files, or device uploads and your product is reading the raw pile.

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Ways to work together

01

Join your team

A senior engineer inside your team. I work in your codebase, standups and code reviews, and stay long enough to own a real part of the product. Under the hood: Laravel, Node.js, Python or AWS.

Full-time or long contract
02

Work solely for your company

A dedicated engineer for one company. I take the product from planning to the live version: backend, data, hosting and screens, with your product as my focus.

Dedicated contract
03

One project

A clearly defined build, then a clean handover: an MVP, taking an AI-built prototype (Lovable, Bolt, Cursor) to production, a migration, or an AI feature. Get in touch to discuss scope and pricing.

Fixed-scope project