Practical takes on data engineering, building scalable systems, and what it really takes to run production platforms
Code shows what. Docs show why. Build an MCP server that automatically surfaces API docs, architecture decisions, and runbooks alongside code for complete context.
ADF is a solid starting point for Azure data integration - but it's not built for every workload. Learn the real-world limits teams encounter in production and when to evolve beyond it.
Airflow dominated for years, but Dagster and Prefect are changing the game. Learn which orchestration tool fits your team's needs - and when it's finally time to say goodbye to that cron job.
Insights from our team of data platform experts
Your git history knows why code exists, not just what it does. Build an MCP server that surfaces commit history, blame data, and change patterns to give Claude temporal context.
Modern codebases span multiple repositories. Learn how to build an MCP server that intelligently loads context across repos without burning 50,000+ tokens on duplicate dependencies.
Stop loading entire files into context. Build an MCP server that automatically loads only the code you need, cutting token usage by 70-90%.
Every wasted token in your AI coding workflow is money out of your budget. Learn to identify token waste, measure its true cost, and understand the hidden productivity costs.
Hard-earned insights from building production data pipelines at scale. Learn about common pitfalls, architecture patterns, and best practices that actually work in the real world.
Session handoffs waste 10,000+ tokens re-explaining context. Learn how to build an MCP server that implements a Handoff Protocol to reduce context transfer to under 2,000 tokens.
Tech debt in data platforms compounds faster and costs more than in traditional software. Learn to identify, measure, and tackle the hidden costs that are slowing down your data team and business.
The medallion architecture has become the default pattern for data lakes, but it's not a silver bullet. Learn the real-world pitfalls teams encounter and how to avoid them.
Learn how to build comprehensive observability into your data platform to catch issues before they impact stakeholders.
Learn how to build a modern data stack from scratch, covering architecture patterns, tool selection, and best practices for data platform engineering.