Latest Thinking

Data Engineering &
Machine Learning Insights

Exploring the architecture of reliable systems and the art of feature engineering.

David Owino Dec 28, 2025

Data Contracts: Architecting Reliability in Distributed Systems

The biggest point of failure in modern data pipelines isn't the code—it's the upstream schema change. Data Contracts act as a formal agreement between software engineers (producers) and data engineers (consumers) to ensure pipeline stability.

The End of "Silent Failures"

By implementing a contract layer using Protobuf or JSON Schema, we prevent breaking changes from ever reaching the Data Lake. If a software service attempts to drop a column or change a data type that the contract forbids, the CI/CD pipeline fails immediately at the source.

Engineering Core: "Data Contracts shift data quality 'Left'—moving it from a post-mortem cleanup task to a pre-deployment requirement."

David Owino Dec 28, 2025

Beyond the Black Box: The Art of Feature Engineering

While most beginners focus on which model to pick, senior data scientists know that Feature Engineering is the true differentiator. A simple Linear Regression with high-quality features will almost always outperform a complex Neural Network with "noisy" data.

The Signal in the Noise

Machine Learning is not about feeding a machine more data; it's about feeding it meaningful data. This involves transforming raw inputs into technical "signals" that the algorithm can actually interpret.

In the rush to deploy Random Forests or Neural Networks, many practitioners overlook the most critical step: Feature Engineering. Algorithms are just engines; your data is the fuel. If the fuel is dirty, the engine stalls.

Why Feature Engineering Matters

Feature engineering is the process of using domain knowledge to create variables that help machine learning algorithms learn better. It is the difference between a 70% accuracy model and a 99% precision system.

Perspective: "In production, model explainability (XAI) is often more valuable than a 1% increase in accuracy."

Technical Archive

Dec 05, 2025

Data Visualization Strategies

A summary of univariate and bivariate analysis techniques using Seaborn and Matplotlib...

Read Full Details

To visualize distributions effectively, we utilize KDE plots for density and Box plots for outliers. High-dimensional data requires FacetGrids to maintain clarity.

Visualization Example
Oct 15, 2024

The Architect's Guide to Data Viz

In the modern business landscape, data is the "new oil." But raw oil is useless until refined...

Read Full Details

The most critical stage of refinement isn't just complex SQL queries—it's the storytelling. We use Radar Charts for performance tracking and Violin Plots for probability density.

Chart 1 Chart 2
Sep 10, 2024

Optimizing SQL for BigQuery

Strategies for reducing query costs and execution time in partitioned datasets...

Read Full Details

Nesting data correctly and choosing the right partitioning keys (usually date-based) reduces the amount of data scanned by up to 90%.

Load More Articles

Quick Reference

Univariate (One Variable)
  • Histogram sns.histplot()
  • Box Plot sns.boxplot()
  • Density Plot sns.kdeplot()
  • Violin Plot sns.violinplot()
  • Bar Chart sns.barplot()
Bivariate (Two Variables)
  • Scatter Plot sns.scatterplot()
  • Line Chart plt.plot()
  • Heatmap sns.heatmap()
  • Bubble Chart plt.scatter()
  • Hexbin Plot sns.jointplot()
Multivariate (3+ Variables)
  • Pair Plot sns.pairplot()
  • 3D Scatter Axes3D.scatter()
  • Treemap squarify.plot()
  • Radar Chart plt.plot()
  • Facet Grid sns.FacetGrid()

Get In Touch

Have a data project or a job opportunity? Let's discuss.

Message sent successfully!