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TUR Tech Pulse — follow updates from the tools I use and recommend. Occasional, curated.

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, topics, 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. Blog posts cover implementation details, tool comparisons, and lessons learned. If you're looking for something specific, the blog index and portfolio offer alternative ways to explore.

Projects

End-to-End Analytics Platform — Databricks, Streamlit & GPT-4o

production
Full-stack data intelligence system for a global manufacturer. Medallion architecture on Databricks, AI-powered natural language querying, z-score anomaly detection, and 10 production dashboards covering 14 markets — shipped in 12 weeks.

Blog Posts

Routing Tables for Content and Data Agents

field note ·
Copy-ready routing tables for content and data agents — task lanes, model tiers, and approval gates using the same A–D discipline as coding agents.

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 vs Zapier vs Make.com vs sim.ai: Which Workflow Automation Tool is Best in 2025?

article ·
Comprehensive comparison of the top workflow automation platforms: n8n, Zapier, Make.com, and sim.ai. Find the perfect tool for your automation needs with real-world examples, pricing analysis, and expert recommendations from a data engineer.