Posts - Page 33
All the articles I've posted, page 33.
- 5 MIN READ•Feb 18, 2026
Testing Data Pipelines: What to Validate and When
Ask an application developer how they test their code and they'll describe unit tests, integration tests, CI/CD pipelines, and coverage metrics. Ask a data e...
data engineeringbest practicestesting - 5 MIN READ•Feb 18, 2026
Denormalization: When and Why to Flatten Your Data
Normalization is the first rule taught in database design. Eliminate redundancy. Store each fact once. Use foreign keys. It's the right rule for transactiona...
data modelingdata engineeringdenormalization - 5 MIN READ•Feb 18, 2026
Headless BI: How a Universal Semantic Layer Replaces Tool-Specific Models
Your organization uses Tableau for executive dashboards, Power BI for operational reports, and Python notebooks for data science. Revenue is defined in Table...
semantic layerdata engineeringheadless BI - 5 MIN READ•Feb 18, 2026
Partition and Organize Data for Performance
A table with 500 million rows takes 45 seconds to query. After partitioning it by date, the same query : filtering on a single day, returns in 2 seconds.
data engineeringbest practicespartitioning - 5 MIN READ•Feb 18, 2026
Data Modeling for Analytics: Optimize for Queries, Not Transactions
The data model that runs your production application is almost never the right model for analytics. Transactional systems are designed for fast writes : inse...
data modelingdata engineeringanalytics - 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.
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