Command: Hey ChatGPT, what does Mr. Joe mean by “The Convergence” in Chapter 3 of his upcoming book? I saw the title and got curious — would really appreciate a bit more context!
Answer:
In the title of Chapter 3 — "Brief History Lessons, The Convergence, and Mixed Model Arts" — "The Convergence" likely refers to the coming together or blending of different data modeling approaches, tools, and roles, which were previously separate or used in isolation.
Here are a few interpretations based on current trends in data modeling:
1. Convergence of Data Modeling Paradigms
Different modeling approaches that were traditionally used for separate purposes are now merging, such as:
- Entity-Relationship modeling (used in relational systems)
- Dimensional modeling (e.g., Kimball methodology for analytics)
- Event-driven modeling (based on modeling around events)
- Modeling for AI/ML (including structured & unstructured data)
Today, modern systems often require a hybrid approach that combines elements from these different styles.
2. Convergence of Tools and Technologies
Traditional SQL-based systems and modern NoSQL / streaming systems are being modeled together.
Tools like dbt, data catalogs, and metadata layers are now part of the modeling process.
Cloud platforms unify storage, transformation, and querying, making data modeling span across layers.
3. Convergence of Roles
Data engineers, analysts, scientists, and business users are increasingly collaborating on data modeling.
It's no longer just the job of a "data modeler" — it’s a shared responsibility across the modern data team.
In short:
"The Convergence" refers to the integration of methods, tools, and people in the evolving practice of data modeling. It's about breaking down silos and creating a unified approach that fits today’s diverse and fast-changing data landscape.
Command: Hey ChatGPT, what does Mr. Joe mean by “The Convergence” in Chapter 3 of his upcoming book? I saw the title and got curious — would really appreciate a bit more context!
Answer:
In the title of Chapter 3 — "Brief History Lessons, The Convergence, and Mixed Model Arts" — "The Convergence" likely refers to the coming together or blending of different data modeling approaches, tools, and roles, which were previously separate or used in isolation.
Here are a few interpretations based on current trends in data modeling:
1. Convergence of Data Modeling Paradigms
Different modeling approaches that were traditionally used for separate purposes are now merging, such as:
- Entity-Relationship modeling (used in relational systems)
- Dimensional modeling (e.g., Kimball methodology for analytics)
- Event-driven modeling (based on modeling around events)
- Modeling for AI/ML (including structured & unstructured data)
Today, modern systems often require a hybrid approach that combines elements from these different styles.
2. Convergence of Tools and Technologies
Traditional SQL-based systems and modern NoSQL / streaming systems are being modeled together.
Tools like dbt, data catalogs, and metadata layers are now part of the modeling process.
Cloud platforms unify storage, transformation, and querying, making data modeling span across layers.
3. Convergence of Roles
Data engineers, analysts, scientists, and business users are increasingly collaborating on data modeling.
It's no longer just the job of a "data modeler" — it’s a shared responsibility across the modern data team.
In short:
"The Convergence" refers to the integration of methods, tools, and people in the evolving practice of data modeling. It's about breaking down silos and creating a unified approach that fits today’s diverse and fast-changing data landscape.
Convergence as a concept excites me!
Great content as always Joe. Thanks.