Technical articles on Cloud Data Engineering, MarTech, and AI.
This is the second post in a series on building an enterprise-grade data pipeline, focusing on the transformation stage using Google Dataform and BigQuery.
I’ll start by saying that this is the first in a series of posts on building an enterprise-grade data pipeline.
A comparative analysis of communication efficiency and FinOps between using an HTTP API and the Model Context Protocol (MCP) for Retrieval-Augmented Generation (RAG) on Cloud Run.
A comprehensive approach to transforming raw NCAA historical basketball data into clean, tidy formats optimized for predictive modeling in March Madness tournaments.
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Data Science Sports Analytics Kaggle Data Transformation Exploratory data analysis of Walmart daily sales data for the M5 Forecasting competition, examining hierarchical time-series structures, seasonality, and sales velocity.
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Data Science Time Series Kaggle EDA An in-depth exploratory data analysis of the Jane Street Market Prediction dataset, structured by stock symbols, highlighting features distribution, correlations, and temporal patterns.
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Data Science Finance Kaggle EDA Explore simple, cost-effective serverless patterns to extract data and load it into BigQuery on Google Cloud.