Microsoft Fabric
dbt Meets ERD: A Unified View of Your Data Landscape in SqlDBM
SqlDBM now shows you what dbt can’t — the full story of your data When it comes to database transformations, the dbt framework is practically an industry standard. Dbt helps teams transform raw data into clean, auditable data assets while incorporating software engineering best practices, such as modularity, testing, and CI/CD, into the analytics layer. […]
John Giles’ Data Model Patterns — Now Available in SqlDBM
At SqlDBM, we believe the best data modeling starts with understanding the business — not reinventing the wheel. That’s why we’re excited to announce that we’ve partnered with John Giles to bring his proven Data Model Patterns into SqlDBM, giving our users a powerful head start on their modeling projects. Who Is John Giles? John […]
SqlDBM Model Governance vs Data Catalogs: does your organization need both?
When evaluating data management tools, a common question arises: should we invest in a dedicated data catalog like Collibra, or can a modern data modeling platform like SqlDBM cover our governance needs? The short answer: yes (and yes)! SqlDBM can serve as both a standalone governance solution for teams building their data practice from the […]
Mastering data type aliases: migrations, modeling, and Snowflake synchronization
In the diverse ecosystem of relational databases, data type aliases are a common yet valuable feature. Simply put, an alias, also known as a synonym, is another name for a foundational system data type. For example, INTEGER might be the default system type, and aliases may include INT and INT4 (as in Postgres). Here […]
SqlDBM Copilot: Embedded AI for Modern Data Modeling
SqlDBM Copilot is an AI assistant built directly into the data modeling environment. Instead of acting as an external chatbot, it lives inside the modeling workflow, understands your projects, and works with your existing schemas, objects, and metadata. With it, you can move faster from ideas to models, automate repetitive work, and keep documentation and […]
SqlDBM Global Standards – Enforcing Data Modeling Consistency Across Enterprise Teams
Data modeling has always been recognized as a collaborative effort—a team sport that reaches its full potential when it incorporates expertise from across the organization. However, many enterprise data modeling initiatives face a fundamental challenge: inconsistency. When teams work on different projects, they often create their own conventions, resulting in fragmented and inconsistent […]
The ROI of Data Modeling – Speaking to the C-Suite Using Business Metrics
Data modeling has long been relegated to the technical corners of organizations, viewed as a necessary evil that slows down delivery and adds complexity to projects. However, this perspective fundamentally misunderstands what data modeling truly represents and the transformative value it can deliver to enterprise organizations. When we strip away the technical jargon and focus […]
The Secret Life of Keys
Most people who have come into contact with a database will be familiar with basic concepts like primary and foreign keys. Yet, many people, including many engineers, lack a formal background in database modeling, let alone set theory, and may be unfamiliar with the many key-related terms used by other team members and in online […]
Conceptual, Logical, Physical Data Modeling — What’s the Difference?
Data modeling is the craft of turning messy, real‑world questions into data that systems can trust and people can use. This article walks through the three classic layers of that craft—conceptual, logical, and physical modeling—showing how each one tightens the focus from broad business meaning to platform‑specific DDL while ensuring the integrity, performance, and governance that the organization […]
From the Birth of Data Modeling to AI – Milestones and Timeline
These days, powerful cloud data platforms like Snowflake, Databricks, and BigQuery empower business-critical use cases from petabyte-scale analytics to cross-cloud data lakes and machine learning. But how did we get here? Many young data engineers might be surprised to learn that the journey of databases as we know them today dates back to the 1970s. […]