Build modular, tested, documented data transformations with dbt.
Tag: Data Engineering
Tips and Tricks – Partition Large Tables for Query Performance
Use table partitioning to dramatically speed up queries on large datasets.
Tips and Tricks – Use Window Functions for Running Calculations
Calculate running totals, rankings, and moving averages efficiently with SQL window functions.
BigQuery Unleashed: Building Enterprise Data Warehouses That Scale to Petabytes
Introduction: BigQuery stands as Google Cloud’s crown jewel—a serverless, petabyte-scale data warehouse that has fundamentally changed how enterprises approach analytics. This comprehensive guide explores BigQuery’s enterprise capabilities, from columnar storage and slot-based execution to advanced features like BigQuery ML, BI Engine, and real-time streaming. After architecting data platforms across all major cloud providers, I’ve found… Continue reading
Spark Isn’t Magic: What Twenty Years of Data Engineering Taught Me About Distributed Processing
Every few years, a technology emerges that fundamentally changes how we think about data processing. MapReduce did it in 2004. Apache Spark did it in 2014. And after spending two decades building data pipelines across enterprises of every size, I’ve learned that the difference between a successful Spark implementation and a failed one rarely comes… Continue reading
The Modern Data Engineer’s Toolkit: Why Python Became the Lingua Franca of Data Pipelines
Last year, I faced a challenge that forced me to rethink everything I knew about The Modern Data Engineer’s Toolkit. What started as a simple optimization project revealed fundamental gaps in my understanding. Let me share what I learned. The Challenge I was building [specific context] when I hit [specific problem]. The standard approaches didn’t… Continue reading
Why Kafka Became the Backbone of Modern Data Architecture: Lessons from Building Event-Driven Systems at Scale
When LinkedIn open-sourced Kafka in 2011, few predicted it would become the de facto standard for real-time data streaming. Fourteen years later, Kafka processes trillions of messages daily across organizations of every size, from startups to Fortune 500 companies. Having architected event-driven systems for over two decades, I’ve watched Kafka evolve from an interesting alternative… Continue reading
The Python Renaissance: Why 2025 Is the Year Everything Changed for Data Engineers
Something remarkable happened in the Python ecosystem over the past year. After decades of incremental improvements, we’ve witnessed a fundamental shift in how data engineers approach their craft. The tools we use, the patterns we follow, and even the way we think about data pipelines have all undergone a transformation that I believe marks a… Continue reading
Building the Modern Data Stack: How Spark, Kafka, and dbt Transformed Data Engineering
The data engineering landscape has undergone a fundamental transformation over the past decade. What once required massive Hadoop clusters and specialized MapReduce expertise has evolved into a sophisticated ecosystem of purpose-built tools that work together seamlessly. Having architected data platforms across multiple industries, I’ve witnessed this evolution firsthand and can attest that understanding how these… Continue reading
Azure Machine Learning: A Solutions Architect’s Guide to Enterprise MLOps
The journey from experimental machine learning models to production-ready AI systems represents one of the most challenging transitions in modern software engineering. Having spent over two decades architecting enterprise solutions, I’ve witnessed the evolution from manual model deployment to sophisticated MLOps platforms. Azure Machine Learning stands at the forefront of this transformation, offering a comprehensive… Continue reading