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2026-02-19 • Alex Merced

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? Dat...

2026-02-19 • Alex Merced

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 ...

2026-02-19 • Alex Merced

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 eve...

2026-02-19 • Alex Merced

Data Modeling for the Lakehouse: What Changes

Traditional data modeling assumed you controlled the database. You defined schemas up front, enforced foreign keys at wr...

2026-02-19 • Alex Merced

Dimensional Modeling: Facts, Dimensions, and Grains

Dimensional modeling is the most widely used approach for organizing analytics data. Developed by Ralph Kimball, it stru...

2026-02-19 • Alex Merced

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 ...

2026-02-19 • Alex Merced

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 system...

2026-02-19 • Alex Merced

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....

2026-02-19 • Alex Merced

Data Vault Modeling: Hubs, Links, and Satellites

Dimensional modeling works well when your source systems are stable and your business questions are predictable. But wha...

2026-02-19 • Alex Merced

Data Modeling Best Practices: 7 Mistakes to Avoid

A bad data model doesn't announce itself. It hides behind slow dashboards, conflicting numbers, confused analysts, and A...

2026-02-19 • Alex Merced

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...

2026-02-19 • Alex Merced

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, tr...

2026-02-19 • Alex Merced

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:...

2026-02-19 • Alex Merced

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. Th...

2026-02-19 • Alex Merced

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, t...

2026-02-19 • Alex Merced

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, an...

2026-02-19 • Alex Merced

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 ...

2026-02-19 • Alex Merced

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 pipeline...

2026-02-19 • Alex Merced

Pipeline Observability: Know When Things Break

An analyst messages you on Slack: "The revenue numbers look wrong. Is the pipeline broken?" You check the orchestrator —...

2026-02-19 • Alex Merced

Data Engineering Best Practices: The Complete Checklist

Best practices documents are easy to write and hard to use. They list principles without context, advice without priorit...