Data modeling is the process of creating a simplified representation of complex real-world data structures and relationships, often using pretty little boxes and lines that make the marketing department ooh and ahh. It's a necessary evil for building applications that can actually do something useful with all that big data everyone keeps talking about.

How to use it in a sentence

• "I spent all day in meetings arguing about the data model for our new AI-powered, blockchain-based, cloud-native, serverless, low-code platform that's going to disrupt the entire industry... if we ever actually build the damn thing."

• "Our genius product manager handed me a napkin sketch and said, 'Just turn this into the data model, bro!' I'm pretty sure he thinks data modeling is some kind of dark magic."

If you actually want to learn more...

• Bounded Context is a key pattern in Domain-Driven Design that helps manage complexity in large data models by dividing them into explicit contexts with clear relationships. Learn more in this article: Bounded Context

• Evolutionary Database Design techniques allow data models to evolve incrementally during agile development, avoiding the dreaded "big bang" database migration. Check out this in-depth guide: Evolutionary Database Design

• Different data models are suited for different types of data - relational models excel at tabular data, while hierarchical models like XML are better for complex documents. Get an overview of various data models and their use cases: Data Models

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