#data-modeling

The Hard Parts of Dimensional Modeling

Grain, multi-valued dimensions, temporal facts and the edge cases that break otherwise correct analytical models

New Video Drop: What Is the Grain? The Most Important Data Modeling Question.

A table can have the right columns, the right types, and the right joins — and still be completely wrong.

Graph Data Modelling: A Functional Framework for Getting It Right

Using Oil & Gas subsurface data as a real-world case study

Best Data Modeling Tools for Snowflake (2026): SQLDBM, dbt, erwin & ER/Studio Compared

Snowflake keeps eating the data warehouse market, and the tooling around it has finally caught up. In 2026, modeling for Snowflake is no…

Star Schema vs Snowflake Schema in Power BI

When to Use Each

Source Modeling: The Missing Workflow

Every data platform assumes the source model is a given. In banks, it almost never is — here’s the workflow nobody named and the role…

dbt Snapshots Explained: Track How Your Data Changes Over Time [dbt Series #6]

A hands-on guide to building Type 2 slowly changing dimensions with dbt and DuckDB

Why your SCD2 model is missing changes

How a NULL-to-value transition silently broke our SCD2 change and how we tracked it down

Bridging the Semantic Gap: How We Built a Custom Framework to Integrate AtScale and Collibra

As modern data architectures grow increasingly complex — spanning data lakes, cloud data warehouses, and numerous BI tools — the need for a…

ER and EER Diagrams for Beginners: A Practical Guide to Database Design

Every well-built database starts life as a drawing before it becomes a single line of SQL. That drawing is usually an ER…

DBT Seeds Explained: Stop Hardcoding Lookups in a Table in DBT [dbt Series #5]

A practical guide to loading reference data into your warehouse with dbt seed and joining it in staging models

Forty Years of Data Modeling: From ER Diagrams to Graph Queries, and What We’re Actually Building

ER Diagrams

Securing the Checkout: A Threat Modeling Case Study

Context: We performed a security analysis on our e-commerce platform (React, Node.js, PostgreSQL, Stripe) to identify and mitigate critical…

From Business Systems to Enterprise AI: The Real Rebuild Nobody Told You About

What most companies got wrong is not the model. It’s that they thought plugging in a model was the point.

Fraud Changes Over Time. Your Architecture Must Keep Up.

Fraud is not static. Relationships form, evolve, and disappear over time. A connection that indicated risk last week may be irrelevant…

What Soccer Taught Me About Machine Learning

Why the world’s most popular game is the perfect laboratory for understanding predictive modeling and data science.

มุมมองใหม่สู่โมเดลเชิงสถิติในระบบผลลัพธ์

สรุปสั้น: การทำความเข้าใจระบบผลลัพธ์ดิจิทัลอย่างลึกซึ้ง ไม่ได้ขึ้นอยู่กับการมองหารูปแบบในระยะสั้น…

Why Finance Data Needs More Than a Star Schema

Every data engineer learns the star schema. One fact table. Dimensions around it. Clean, simple, fast.

The Question You Forgot to Ask: How Business Questions Shape Data Modeling in Legal Tech?

The scene

From Data Tables to Business Insights: Building a Profit Analysis Dashboard in Power BI

Introduction

The Design Problem Was Always The Hard Part

Introducing the IRiS Assistant.

Draw.io vs Lucidchart vs ChartDB: Guide for Best ERD Tool

You know that feeling when you join a new project and ask, “Okay, so how does the database actually work?”, and someone sends you a…

Optimizing Data Vault in BigQuery: Why Physical Clustering Matters More Than Hash-Based Keys

How shifting from hash-based business keys to structured numeric clustering strategies can fundamentally improve pruning, storage…

Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world

How Airbnb’s data engineers and analytics engineers built a consistent and flexible data modeling framework to support the expansion into…

Building an Ontology for a Knowledge Graph Is a Process, Not a Diagram

Five established approaches, and the ongoing process that keeps them working.

SCD Type 2 in PySpark — Comparing the Join and Union + Lead Approaches

If you have ever needed slowly changing dimension (SCD) Type 2 logic in PySpark and reached for a homegrown solution, you have probably…

Como decidir sua modelagem e entrega de dados

Star Schema vs One Big Table, quando engenharia de dados vira questão de virtude e esquecemos que a resposta está nos gargalos do negócio.

AI-Powered Healthcare Data Modeling: Generate Data Models in 30 seconds

Data Modeling using AI, Design Healthcare Data Model with AI. Star Schema, Snowflakes, OLTP, OLAP, Medallion Architecture, and more.

Data Modeling in SQL: What It Is, Types, and How to Do It

A few years ago, I spent days trying to debug a report that kept giving us the wrong numbers. The queries were fine. The database was live…

Data Modeling for Data Engineers: A Step-by-Step Framework for Fact and Dimension Tables

If you have ever looked at a data modelling question in an interview or at work and had no idea where to begin, this one is for you.

Complete Guide to Snowflake Streams and Tasks with Real-Time Examples

Introduction

Null Isn’t the Issue

Null isn’t the issue. It’s when platforms simplify missing data by turning it into values like 0, empty text, or false. These defaults may…

Sistemleşme: Veri Bilimi Modelinin Gerçek Sınavı

Herkese Merhaba,

When history fails you, borrow from geography

How Airbnb used sequential geographic recovery signals and prior propagation to generate reliable corridor-level forecasts when local data…

Data Engineer Interview Question : Designing a High‑Level OLAP Data Model for Social Media Emoji…

Imagine sitting in a tough data‑engineering interview and the interviewer says:

Three ways to model the same data, and when each one earns its keep

I’ve spent a long time building data platforms, and one argument keeps coming back on every team I’ve worked with: *how should we model…

Building a Semantic Data Modeling Agent: From Fragmented Enterprise Schemas to a Unified Canonical…

Introduction

dbt Core v1.12 Rewrites the Semantic Layer Spec

Metrics Now Live Where Your Data Lives

Data Vault Explained: The Data Modeling Approach Built for Messy, Inconsistent, and Multi-Source…

A side-by-side breakdown of a real e-commerce ID conflict problem

The Architect’s Blueprint: A Guide to Kimball Data Modeling: Part 3 — Types of Fact Table and…

In my decade of experience as DW lead, I have witnessed a lot of technical debt and serious Data Warehouse problems caused by a lack of…

Entity Isolation: The Force Multiplier Behind MESA’s Cost Savings

By Steven Passanante

Understanding Data Modelling with Mongoose: The Foundation of Scalable Backend Systems

When most developers begin backend development, databases feel simple -create a collection, store some data, fetch it later.

What is Data Analytics

Data Analytics is the process of collecting, cleaning, organizing, and analyzing data to discover useful insights and support better…

POLE+O: The 5-Type Ontology That Solves the Hardest Part of Building a Knowledge Graph

Neo4j’s Create Context Graph ships with a universal starting schema. Here’s why starting with 5 node types beats starting from scratch.

The dbt Architecture That Keeps Your SQL Manageable [DBT Series #3]

A practical guide to staging, intermediate, and marts, as well as how ref() and source() wire it all together

What Are dbt Seeds? (The Simplest Explanation)

Continuing the series on the DBT , lets deep dive into dbt seeds

Sequential Data Modeling: Text Generation with RNNs

SQL Boo-Boos #3: Why My Row Count Doubled After a Join (And Why That’s Way More Dangerous Than You…

Let me paint a picture.

What a Preprint Can’t Tell Us About Hantavirus’ Contagiousness

Data Modeling for Aspiring Data Engineers (Part 1): Data warehouses

Are you a data engineer — or aspiring to become one? This article is for you.