Skip to content
We build e-commerce, healthcare & law websites, AI agents, and LLM apps for startups and agencies.Start a project
All work
Data

Pipelines your whole team can trust

We built DataForge a platform that turns scattered, unreliable sources into trusted, real-time pipelines — so every team makes decisions on numbers they can actually believe.

DataForge — Pipelines your whole team can trust
Client
DataForge
Timeline
16 weeks
Our role
Data engineering, platform, tooling
Platforms
Cloud, Dashboard, API

Overview

Three teams, three different truths

Every team at DataForge had its own copy of the numbers, and none of them agreed. Reports contradicted each other, trust in the data eroded, and simple questions took days to answer.

Highlights

  • A unified pipeline from raw sources to trusted tables
  • Data quality tests and freshness checks on every model
  • Real-time streaming for the metrics that can't wait
  • Self-serve access so teams answer their own questions

Impact

1

Source of truth

-80%

Time to a trusted answer

99.5%

Pipeline reliability

200+

Tested data models

01

The challenge

When people can't trust the data, they stop using it — and start guessing. DataForge needed one reliable pipeline and the tests to prove, continuously, that it stayed correct.

02

Our approach

We consolidated sources into a modeled warehouse, wrapped every model in quality and freshness tests, added streaming for time-sensitive metrics, and opened self-serve access with guardrails.

03

The outcome

DataForge now runs on a single source of truth that teams trust — answers that took days take minutes, and the pipeline proves its own reliability on every run.

What we built

The pieces that made it work

Unified pipeline

Raw sources modeled into clean, trusted tables.

Quality tests

Every model checked for correctness and freshness.

Streaming

Real-time metrics for decisions that can't wait for a batch.

Self-serve

Teams explore and answer their own questions safely.

Lineage

Full traceability from a number back to its source.

Alerting

Broken data pages the right owner before it spreads.

Under the hood

SnowflakedbtAirflowKafkaPythonBigQueryLookerPostgreSQL
For the first time, every team is arguing about what to do — not about whose numbers are right.
Nadia Okonkwo, Head of Data· DataForge

Have a build like this in mind?

Tell us what you're building — within one business day you'll have a suggested scope, a timeline, and a ballpark.