Pipelines your whole team can trust
DataForge turns scattered sources into trusted, real-time pipelines - and makes shipping a new one a task, not a project. We built the architecture, the tooling and the observability so data the business relies on is fresh, correct and easy to reason about.
Client
DataForge
Engagement
Ongoing partnership
99.99%
Pipeline uptime
on critical paths
6x
Faster to ship
a new pipeline
40+
Sources unified
into one platform
-45%
Data infra cost
right-sized
Every team wanted data, and nobody trusted the numbers. Sources were scattered, pipelines were brittle, and a new one took weeks. DataForge needed a platform where pipelines are reproducible, freshness is visible, and shipping a new source is a task a single engineer can finish - so the business can finally believe its dashboards.
Built in layers
Each layer does one job well and can be reviewed, rebuilt and reasoned about on its own.
Ingest
Sources are pulled in reproducibly, with schema and freshness tracked from the start.
Transform
Clean, validated transformations turn raw data into trusted models.
Store
Curated data lands in a warehouse that's versioned and easy to query.
Serve
Fresh, correct data is served to dashboards and products the business relies on.
Data nobody quite believed
Scattered sources, brittle jobs and silent failures meant dashboards disagreed and leaders hedged every decision. Building a new pipeline was a multi-week project, so requests piled up. DataForge needed trusted, fresh data and a platform where new pipelines ship in days.
Reproducible pipelines, visible freshness
We built the platform in layers - ingest, transform, store, serve - each reproducible from code and observable on its own. Freshness and quality are surfaced as first-class signals, and a new pipeline reuses the same building blocks, so shipping one is a task instead of a project.
Trusted data, shipped in days
DataForge now runs the pipelines the business depends on with visible freshness and near-perfect uptime. Teams believe the numbers because they can see how fresh they are, and a new source goes from request to production in days, not weeks.
Systems we put in place
Layered architecture
Ingest, transform, store and serve are separate, reproducible layers you can reason about.
Visible freshness
Every dataset shows how fresh it is, so teams know exactly how much to trust it.
Reusable building blocks
New pipelines reuse the same tested components, turning a project into a task.
Quality checks in-line
Data is validated as it flows, so bad records are caught before they reach a dashboard.
Observability throughout
Metrics and lineage on every stage make failures searchable, not mysterious.
Cost-aware by design
Resources are right-sized and tagged, so spend maps to the data it produces.
Pipelines
Storage
Operate
“For the first time, our leadership trusts the dashboards. DataForge made freshness something you can actually see.”
Services behind this work
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