Posts - Page 34
All the articles I've posted, page 34.
- 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.
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
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 - 5 MIN READ•Feb 18, 2026
Semantic Layer vs. Metrics Layer: What's the Difference?
Both terms appear in every modern data architecture diagram. They're used interchangeably in conference talks, Slack threads, and vendor marketing. And almos...
semantic layerdata engineeringmetrics layer - 5 MIN READ•Feb 18, 2026
How to Design Reliable Data Pipelines
Most pipeline failures aren't caused by bad code. They're caused by no architecture. A script that reads from an API, transforms JSON, and writes to a databa...
data engineeringbest practicespipelines - 5 MIN READ•Feb 18, 2026
Conceptual, Logical, and Physical Data Models Explained
Most data teams jump straight from a stakeholder request to creating database tables. They skip the planning steps that prevent misalignment, redundancy, and...
data modelingdata engineeringconceptual model - 5 MIN READ•Feb 18, 2026
How to Build a Semantic Layer: A Step-by-Step Guide
Most teams start building a semantic layer the wrong way: they open their BI tool, create a few calculated fields, and call it done. Six months later, three ...
semantic layerdata engineeringarchitecture - 5 MIN READ•Feb 18, 2026
How to Think Like a Data Engineer
The median lifespan of a popular data tool is about three years. The tool you master today may be deprecated or replaced by the time your next project ships....
data engineeringbest practicespipelines - 5 MIN READ•Feb 18, 2026
What Is Data Modeling? A Complete Guide
Every database, data warehouse, and data lakehouse starts with the same question: how should this data be organized? Data modeling answers that question by c...
data modelingdata engineeringdata lakehouse - 5 MIN READ•Feb 18, 2026
What Is a Semantic Layer? A Complete Guide
Ask three teams in your company how they calculate "revenue" and you''ll get three answers. Sales counts bookings. Finance counts recognized revenue.
semantic layerdata engineeringdata lakehouse