Building data pipelines by writing raw HTTP requests, database connectors, and hashers from scratch is a massive waste of engineering time. wpipe-steps delivers 196 battle-tested primitives ready for production.
Here is how you use Modular Step Architecture & 196 Pre-Built Catalog in a production pipeline with wpipe-steps:
from wpipe import Pipeline
from wpipe_steps.connectivity import HttpRequestStep
pipeline = Pipeline(pipeline_name="fetch_user_pipeline")
# Instant declarative step composition
pipeline.set_steps([
HttpRequestStep.as_step(
name="fetch_user",
url="https://jsonplaceholder.typicode.com/users/1",
method="GET",
response_key="user_data"
)
])
result = pipeline.run({})
print(result["user_data"]["status_code"])
Why developers love wpipe-steps:
- 196 cataloged steps covering Redis, ClickHouse, MySQL, WAF, S3, Docker, and local HuggingFace AI.
- Lazy-loading imports for instant sub-100ms startup times.
-
Clean
.as_step()factory interface with strict type enforcement.
Check out the full repository on GitHub and install via PyPI!
Author: William Steve Rodríguez Villamizar (Wisrovi)
Top comments (1)
Keeping the orchestration engine lean (<50MB RAM footprint) dramatically changes cost efficiency and predictability across edge and CI/CD environments.
What are your main architectural considerations when weighing lightweight code-first pipelines against monolithic workflow engines?