Platform Economics
Databricks Cost Optimization: Where to Look First
The bill is a symptom. Workload design, compute class, ingestion cadence, and missing ownership are usually the cause.
10 min
Insights
Architecture notes for people who have to live with the platform.
Written for executives, technical leaders, and platform teams. Every article carries an executive summary up front and the technical detail underneath. Not a feature recap, and not a pitch disguised as a white paper.
Executive Strategy
Data economics, AI business cases, and operating models for leadership teams.
Data Architecture
Lakehouse design, modernization, data products, and the decisions that age well.
Migration & Modernization
Databricks Migration Assessment Checklist
A migration fails in inventory, not in Spark. If you cannot name the workloads, owners, and contracts, you are not ready to convert them.
9 min
Databricks Architecture
Databricks vs Traditional Data Warehouse Architecture
The useful comparison is not brand versus brand. It is whether one architecture can support engineering, warehousing, governance, and AI without copying data into a second estate.
9 min
Databricks Architecture
Medallion Architecture: When Bronze, Silver, and Gold Make Sense
Medallion is a quality contract between layers. It is not a mandatory folder taxonomy, and it is not automatically correct because the names are famous.
8 min
Databricks
Lakeflow, Unity Catalog, SQL, AI/BI, and platform practices from production work.
Databricks Architecture
Databricks Architecture Best Practices for Enterprise Teams
A lakehouse becomes an operating layer only when environments, catalogs, workloads, and consumption are designed as one system—not as a growing pile of workspaces.
12 min
Data Engineering
Lakeflow Architecture for Modern Data Engineering
Lakeflow is the engineering surface for ingestion, transformation, streaming, and orchestration. It still needs contracts, quality, and owners.
10 min
Databricks Architecture
Common Databricks Architecture Mistakes
The recurring failures are not exotic. They are workspace sprawl, catalog accident, notebook production, and AI bolted onto untrusted data.
8 min
Enterprise AI
Agents, RAG, governance, evaluation, and the economics of production AI.
AI & ML
How to Prepare Enterprise Data for AI Agents
Agents inherit whatever you give them: permissions, definitions, and quality. Preparing data for agents is lakehouse work, not prompt work.
10 min
Analytics
Forecasting, executive intelligence, and decision systems the business can trust.
Databricks SQL & AI/BI
Building Trusted Executive Analytics on Databricks
Executives do not need more dashboards. They need a small number of numbers that survive contact with finance, operations, and the next question.
9 min
Governance
Security, permissions, lineage, and the controls that make AI safe to scale.
Unity Catalog & Governance
How to Structure Unity Catalog for Enterprise Governance
Catalogs, schemas, groups, and ownership are the governance architecture. Privileges accumulated on individual users are how that architecture decays.
11 min
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused