Solutions / Data Infrastructure
Build the data path once. Reuse it across the stack.
Replace destination-specific ingestion and transformation logic with reusable workflows for collecting, transforming, governing and routing data.
The problem
Every destination shouldn't require another pipeline.
Collection, transformation and routing logic gets rebuilt per destination — duplicated effort, and duplicated places for policy to drift.
Consolidation
One source doesn't have to mean one pipeline per destination.
Per-destination pipelines
The same source, collected and transformed three separate times.
One reusable path
LYFTDATA
Collect · transform · govern · route
- Collect
- Transform
- Route
Destination A
Destination B
Destination C
Collected once. Governed once. Routed to as many destinations as needed.
Two models. In the per-destination model, the same source is collected three separate times, once per destination, each with its own pipeline. In the LyftData model, the source is collected once, passes through one LyftData pipeline, and fans out to multiple governed destinations.
One source does not have to mean one destination — or one pipeline per destination.
Reuse
Reuse patterns instead of rebuilding pipelines.
Move transformation upstream, once, and reuse the pattern the next time a similar source or destination shows up.
Transformation
normalization and enrichment logic written once, not per destination.
Routing
one source can feed several governed outputs without duplicating collection.
Workflows
patterns packaged as reusable blueprints instead of rebuilt pipeline by pipeline.
Data flow
Not a narrow ETL replacement.
LyftData handles the continuous, operational movement, transformation and routing of data — it's not positioned as a blanket replacement for every existing ETL or data-integration tool.
Why LyftData
Independent
no single destination owns the pipeline that feeds it.
Governed
transformation and routing rules apply once, upstream of every consumer.
Reusable
adding a destination is a routing change, not a new pipeline build.
Build the data path once.
Start with LyftData, or map your current pipeline sprawl with the team.