6 articles exploring data engineering
Comprehensive Apache Airflow analysis: open source and every managed vendor (Astronomer, Cloud Composer, MWAA), pricing at three scales, Airflow 3.0 migration, operational pain points, AI workflows, and a decision matrix for choosing the right deployment.
Comprehensive Dagster analysis: pricing, asset-centric orchestration, AI/ML pipelines, learning curve, integrations, and why treating data as the product gives teams lineage and governance that task-centric orchestrators cannot match.
Comprehensive Prefect analysis: pricing, scaling, deployment architecture, integrations, learning curve, and why its dynamic Python-native control flow fits AI agent workflows better than static DAG orchestrators.
After running all three in production: 20-criteria breakdown of real migration costs, team overhead, backfill behaviour, and which orchestrator survives 500+ pipelines.
How to build a SaaS metrics stack that produces ARR, MRR, churn, LTV, and CAC you can actually defend - with SQL, Python, and the right source-of-truth hierarchy.
How to build investor-grade revenue data infrastructure before a Series B raise - the stack, the metrics, the entity resolution problem nobody talks about.