Posts
All the articles I've posted.
- 6 MIN READ•Feb 18, 2026
Data Virtualization and the Semantic Layer: Query Without Copying
Every data pipeline you build to move data from one system to another costs you three things: time to build it, money to run it, and freshness you lose while...
semantic layerdata engineeringdata virtualization - 5 MIN READ•Feb 18, 2026
Batch vs. Streaming: Choose the Right Processing Model
"We need real-time data." This is one of the most expensive sentences in data engineering – because it's rarely true, and implementing it when it's not neede...
data engineeringbest practicesbatch processing - 5 MIN READ•Feb 18, 2026
Slowly Changing Dimensions: Types 1-3 with Examples
Dimensions change. A customer moves cities. A product gets reclassified. An employee changes departments. How your data model handles these changes determine...
data modelingdata engineeringslowly changing dimensions - 6 MIN READ•Feb 18, 2026
The Role of the Semantic Layer in Data Governance
Most organizations have a data governance policy. It lives in a Confluence page. It defines who owns what data, what terms mean, and who should have access. ...
semantic layerdata engineeringdata governance - 4 MIN READ•Feb 18, 2026
Schema Evolution Without Breaking Consumers
A source team renames a column from `user_id` to `customer_id`. Twelve hours later, five dashboards show blank values, two ML pipelines fail, and the data en...
data engineeringbest practicesschema evolution - 5 MIN READ•Feb 18, 2026
Dimensional Modeling: Facts, Dimensions, and Grains
Dimensional modeling is the most widely used approach for organizing analytics data. Developed by Ralph Kimball, it structures data into two types of tables:...
data modelingdata engineeringdimensional modeling - 5 MIN READ•Feb 18, 2026
Why Your AI Initiatives Fail Without a Semantic Layer
Your team builds an AI agent. It connects to your data warehouse. A product manager types "What was revenue last quarter?" and gets a number. The number is w...
semantic layerdata engineeringAI - 4 MIN READ•Feb 18, 2026
Idempotent Pipelines: Build Once, Run Safely Forever
A pipeline runs, processes 100,000 records, and loads them into the target table. Then it fails on a downstream step. The orchestrator retries the entire job...
data engineeringbest practicesidempotency - 5 MIN READ•Feb 18, 2026
Data Modeling for the Lakehouse: What Changes
Traditional data modeling assumed you controlled the database. You defined schemas up front, enforced foreign keys at write time, and optimized with indexes....
data modelingdata engineeringdata lakehouse - 5 MIN READ•Feb 18, 2026
Semantic Layer vs. Data Catalog: Complementary, Not Competing
"We already have a data catalog, so we don't need a semantic layer." This is one of the most common misconceptions in modern data architecture. Catalogs and ...
semantic layerdata engineeringdata catalog - 5 MIN READ•Feb 18, 2026
Data Quality Is a Pipeline Problem, Not a Dashboard Problem
When an analyst finds null values in a revenue column, the typical response is to add a calculated field in the BI tool – `IF revenue IS NULL THEN 0`. That "f...
data engineeringbest practicesdata quality - 5 MIN READ•Feb 18, 2026
Star Schema vs. Snowflake Schema: When to Use Each
Both star schemas and snowflake schemas are dimensional models. They both organize data into fact tables (measurable events) and dimension tables (context ab...
data modelingdata engineeringstar schema