Posts - Page 37
All the articles I've posted, page 37.
- 6 MIN READ•Feb 18, 2026
Data Virtualization and the Semantic Layer: Query Without Copying
How data virtualization and a semantic layer let you query across sources without copying data, plus guidance on when to virtualize and when to materialize.
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
Slowly Changing Dimensions Types 1, 2, and 3 explained with examples: overwrite, add a history row, or add a column, plus how to choose the right SCD type.
data modelingdata engineeringslowly changing dimensions - 6 MIN READ•Feb 18, 2026
The Role of the Semantic Layer in Data Governance
How a semantic layer turns governance policy into practice: shared metric definitions, access rules enforced at query time, lineage, and certification.
semantic layerdata engineeringdata governance - 4 MIN READ•Feb 18, 2026
Schema Evolution Without Breaking Consumers
Treat your schema like an API. Learn which changes are safe or breaking, the additive-only pattern, schema versioning, and contract checks between pipelines.
data engineeringbest practicesschema evolution - 5 MIN READ•Feb 18, 2026
Why Your AI Initiatives Fail Without a Semantic Layer
Five ways AI agents give wrong answers without a semantic layer (metric hallucination, join confusion, column misreads, security bypass) and how to fix them.
semantic layerdata engineeringAI - 5 MIN READ•Feb 18, 2026
Data Modeling for the Lakehouse: What Changes
How data modeling changes in a lakehouse: schema-on-read, Iceberg partition evolution, and a virtual Medallion Architecture built as SQL views over open tables.
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.
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`.
data engineeringbest practicesdata quality - 5 MIN READ•Feb 18, 2026
Semantic Layer vs. Metrics Layer: What's the Difference?
A metrics layer defines calculations; a semantic layer adds business context, joins, and governance. See how they relate and why the distinction matters.
semantic layerdata engineeringmetrics layer - 5 MIN READ•Feb 18, 2026
How to Design Reliable Data Pipelines
Reliable pipelines come from architecture, not bug fixes. Covers the four pipeline layers, building DAGs instead of scripts, dependencies, and failure handling.
data engineeringbest practicespipelines - 5 MIN READ•Feb 18, 2026
How to Build a Semantic Layer: A Step-by-Step Guide
A step-by-step plan for building a semantic layer: start with metrics, map sources, use bronze, silver, and gold views, document, and enforce access policies.
semantic layerdata engineeringarchitecture