Insights

Field notes for leaders and practitioners dealing with the real version of the work.

Enterprise decision-makers start with strategy, architecture, governance, modernization, reliability, and AI readiness. Professional-development content has its own track below, and every article is searchable at the end.

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For Data Leaders

Start with the enterprise pain points.

Best for CTOs, VPs of Data, founders, and operators thinking through governance, audits, AI readiness, cloud migration, schema drift, platform choice, and decision quality. All 17 notes in the library ↓

Databricks cost governance

Don't Buy More Databricks Compute Until You Check These 4 Things

A four-check diagnostic for separating a real capacity need from workload waste, routing problems, and governance drift.

Read insight ↗Legacy migration

The 500-Report Migration Trap

Read this before a systems integrator proposes translating every legacy script into Databricks notebooks line by line.

Read insight ↗Master data and trust

How MDM Broke a Golden Record

For leaders whose single source of truth keeps losing to fourteen typos in the source systems.

Read insight ↗Operating model

The Data Team Maturity Ladder

Use this to tell real maturity from an organization skipping to the last chapter and calling it growth.

Read insight ↗Change management

A Calendar Is Not a Control

Why blanket change freezes create ceremony instead of maturity, and what data teams should build instead.

Read insight ↗AI accountability

Hidden Logic Makes Engineers Scapegoats for Bad AI Outputs

For leaders deciding where business logic should live before AI outputs get blamed on the engineers.

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For Data Professionals

Build a career story with senior-level evidence.

For career switchers and working data professionals who want sharper portfolios, better interview stories, clearer roadmaps, and stronger senior-role readiness. All 14 notes in the library ↓

Portfolio proof

The Data Project That Actually Gets You Hired

What separates a project hiring managers remember from the portfolio everyone else submits.

Read insight ↗Hiring signals

7 Competencies That Get Data Engineers Hired in 2026

The competencies interviewers actually test for, including the one most candidates never mention.

Read insight ↗AI and skill

Why Your AI Assistant Is Making You a Poor Engineer

On the skills that atrophy when the assistant does the thinking, and the one worth building instead.

Read insight ↗Career outlook

Why AI Won't Replace Data Engineers (Yet)

Syntax was never the bottleneck in enterprise data platforms. Judgment still is.

Read insight ↗Senior readiness

The 10-Year Junior

For experienced professionals who look senior on paper but stalled inside one tool's comfort zone.

Read insight ↗Career risk

Why Being Indispensable Is Killing Your Data Career

The hero bottleneck feels like job security and quietly caps the career.

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Technical Field Notes

Use these when the work gets practical.

Hands-on notes for SQL, Python, Databricks, pipelines, ETL and ELT, data warehouse foundations, and the mechanics behind reliable delivery. All 16 notes in the library ↓

Databricks delivery

Your Databricks Notebook Belongs in Production

Why the notebooks-versus-scripts argument is the wrong one, and what production readiness actually requires.

Read insight ↗Lakehouse layers

The Medallion Masterclass

Naming, ownership, access, governance, and when a Platinum layer actually makes sense.

Read insight ↗Databricks cost

Why Default Databricks Settings Are Setting Your Budget on Fire

The defaults that quietly burn compute budget, and how to recover.

Read insight ↗Lakeflow patterns

Implementing Slowly Changing Dimensions with Lakeflow

A long-form walkthrough of SCD patterns with Databricks Lakeflow Pipelines and Connect.

Read insight ↗Data quality

Why I'm Betting on Databricks DQX Over dbt Tests

A lean-stack argument for where data quality checks should run.

Read insight ↗Silent failures

Why "Successful" Pipelines Are Scarier Than Failed Ones

The pipeline that finishes green while quietly destroying trust in the data.

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Technical field notes on video

Chris Gambill | Data Engineering Strategy

Practical technical education and architecture thinking grounded in production work, not tutorial theater.

Visit the channel Interviews and talks →

The whole library

Search every field note.

Every article on the site, newest first. Search by topic, platform, or phrase, or narrow by section.

104 notes

Databricks

Don't Buy More Databricks Compute Until You Check These 4 Things

Sep 12, 2026

A four-check diagnostic for determining whether a Databricks cost spike reflects a real capacity need or an architecture, workload, routing, or governance is…

Read insight ↗Data Strategy