Explains why transactional data models are inefficient for analytics and how to design denormalized, query-optimized models for better performance.
Seven common data modeling mistakes that cause reporting errors and slow analytics, with practical solutions to avoid them.
Explains database denormalization: when to flatten data for faster analytics queries and when to avoid it.
Explains what a semantic layer is, its components, and how it provides consistent business definitions for data queries and AI agents.
Explores how data modeling principles adapt for modern lakehouse architectures using open formats like Apache Iceberg and the Medallion pattern.
Explains the three levels of data modeling (conceptual, logical, physical) and their importance in database design.
Explains the difference between a metrics layer and a semantic layer in data architecture, clarifying their distinct roles and relationship.
Seven critical mistakes that can derail semantic layer projects in data engineering, with practical advice on how to avoid them.
Explains Headless BI and how a universal semantic layer centralizes metric definitions to replace tool-specific models, enabling consistent analytics.
A step-by-step guide to building a robust semantic layer for consistent data metrics, covering architecture, stakeholder alignment, and implementation.
A comprehensive guide to data modeling, explaining its meaning, three abstraction levels, techniques, and importance for modern data systems.
A software architect's humorous account of a client derailing a professional Power BI dashboard project with a chaotic, self-built data model.
A monthly roundup of curated links and articles covering data engineering, Kafka, stream processing, and AI, with top picks highlighted.
An introduction to data modeling concepts, covering OLTP vs OLAP systems, normalization, and common schema designs for data engineering.
A technical exploration of using C# records and collections for immutable data models, covering benefits and practical implementation details.
Explains the CQRS pattern, its benefits for scaling read/write operations independently, and when to use it in software architecture.
A recap of AWS re:Invent 2024 Day 3, covering sessions on advanced DynamoDB data modeling and key AI/Data announcements from Amazon Bedrock.
A critique of modern bug trackers, proposing a 'separation of concerns' principle to better distinguish factual bug records from planning data.
Explores the differences between event and entity data modeling, when to use each approach, and practical design considerations for structuring data effectively.
Explores Data-Oriented Programming principles in Java, focusing on modeling data with records and sealed types.
