Architecting Reliability in Distributed Systems
David Owino
Nov 5,
2025
The most common point of failure in modern data
pipelines is not application code, but unannounced
upstream schema changes.
Data contracts establish a formal
agreement between data producers (software engineers) and data
consumers (data engineers),
ensuring consistency, compatibility, and pipeline
stability.
Engineering principle: data Contracts
shift data quality left, transforming it from a
reactive cleanup task into a proactive, pre-deployment
requirement.
The End of Silent Failures
By introducing a contract layer - using technologies such
as
Protobuf
or JSON Schema - breaking
changes are detected before they reach the data lake.
Any attempt to remove or alter a field fails fast within
the CI/CD pipeline, preventing downstream disruptions.
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Beyond the Black Box: The Art of Feature Engineering
David Owino
Dec 28,
2025
Feature engineering-not model
complexity-drives
machine learning performance, enabling simple
models to outperform complex ones when data quality is
high.
Perspective:
In production, transforming raw data into
meaningful signals
and prioritizing model explainability (XAI) often
delivers more business value than marginal gains in
accuracy.
ML Lifecycle Workflow
-
Feature Extraction
Scikit-Learn
-
Model Selection
XGBoost
-
Hyperparameter Tuning & CV
Optuna
-
Explainability
SHAP / LIME
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