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Content Tagged "Data Engineering"

This page collects all articles and projects tagged Data Engineering. Tags help you navigate by tool, theme, or topic—whether you're looking for content on specific technologies like n8n or Databricks, or broader themes like workflow automation or hockey analytics.

Content spans agentic engineering, production AI agents, data pipelines, full-stack work, and hockey analytics. Browse other tags or case studies for applied examples.

Projects describe case studies with real outcomes—conversational analytics prototypes, AI feedback agents, automated content pipelines, and hockey analytics dashboards. Writing posts cover implementation details, tool comparisons, and lessons learned. If you're looking for something specific, the writing index and portfolio offer alternative ways to explore.

Projects

Governed lakehouse and ML in production — Enterprise data platform

production
Three years building and running an Azure Databricks platform for a global manufacturer: 100 production jobs, 600+ governed tables, NLP across 1.46M support cases, and ML in production.

Writing

n8n Split in Batches Node: Processing Large Datasets Without Hitting Rate Limits

tutorial · · Updated
When you need to process thousands of records in n8n, feeding them all at once into an API or database crashes the workflow or triggers rate limits. The Split in Batches node is how you process large datasets reliably — here is how it actually works.

n8n Merge Node Modes Explained: Combine, Multiplex, and Pass-Through Under Partial Input

tutorial · · Updated
The n8n Merge node has four modes with subtle behavior differences that only surface with partial input, mismatched item counts, or timing gaps between branches. Here is what each mode actually does.

n8n Error Trigger: Workflow-Level Error Catching and When It Actually Fires

tutorial · · Updated
The n8n Error Trigger node is a workflow that runs when another workflow fails — but the when and why are full of edge cases. Here is how it actually works, what it misses, and how to build reliable error handling in production n8n.