Articles
- 路 Datumology 路 AI Strategy
The AI Advantage Formula: Understanding the Real Value of AI Systems
Discover how to calculate the true value of AI systems using the AI Advantage formula, which balances compute scaling and autonomy against implementation costs.
Why We Chose Astro: Performance and Markdown Power
Discover why Datumology selected Astro for its new website, focusing on its incredible performance, seamless Markdown/MDX integration, and developer-friendly features.
- 路 Datumology 路 Data Engineering
Comparing Prefect and Dagster (and why not Airflow)
A detailed comparison of modern workflow orchestration tools Prefect and Dagster, with an explanation of why they might be better alternatives to Airflow for many data engineering use cases.
- 路 Datumology 路 Data Tools
Getting Started with dbt Core
A beginner-friendly guide to setting up and using dbt Core for data transformation in your edge data stack.
DuckDB for Edge Data Analytics
Exploring how DuckDB enables powerful analytics at the edge, bringing data processing closer to where data is generated.
- 路 Justin B 路 Data Strategy
Is Big Data Too Big to Scale?
Examining the reality gap between big data promises and practical business insights, featuring Derek Steer's critical analysis of BI tool limitations.
Why Datumology Chose Cloudflare
Discover Datumology's reasons for selecting Cloudflare as its primary cloud platform, covering performance, developer experience, integrated services, cost-effectiveness, and future-forward innovation.
- 路 Datumology 路 Data Stack
Edge Data Stack: DuckDB, dbt, evidence.dev, and marimo
Exploring a lean, powerful data stack for modern analytics challenges.
- 路 Justin B. 路 Historical Big Data
Kafka in 2016: Early Days of Stream Processing
Exploring the early challenges of implementing Apache Kafka for real-time data streaming in 2016, and how stream processing has evolved to meet modern demands.
- 路 Justin B. 路 Historical Big Data
The Reality of Data Science in the Era of Big Data - Circa 2010
A candid reflection on the realities of early data science work in 2010, where cross-functional challenges and data engineering often overshadowed algorithmic sophistication.
- 路 Justin B. 路 Historical Big Data
Hadoop in 2013: The Promise and Perils of Big Data Processing
A retrospective look at Hadoop deployment challenges from 2013, examining how the ecosystem has evolved and what lessons remain valuable for modern data platforms.
- 路 Justin B. 路 Historical Big Data
Cassandra in 2012: Lessons Learned and Modern Perspectives
Reflecting on Cassandra deployment challenges from 2012, with insights on how the landscape has evolved and what remains relevant today.