Explains the importance of automated testing for data pipelines, covering schema validation, data quality checks, and regression testing.
Explains what a semantic layer is, its components, and how it provides consistent business definitions for data queries and AI agents.
Explains the difference between a metrics layer and a semantic layer in data architecture, clarifying their distinct roles and relationship.
Explains Data Vault data modeling, its core components (Hubs, Links, Satellites), and the problems it solves for complex, evolving data sources.
Explains how a semantic layer enforces data governance by embedding policies directly into the query path, ensuring consistent metrics and access control.
A comprehensive guide to data modeling, explaining its meaning, three abstraction levels, techniques, and importance for modern data systems.
A guide to designing reliable, fault-tolerant data pipelines with architectural principles like idempotency, observability, and DAG-based workflows.
Explains database denormalization: when to flatten data for faster analytics queries and when to avoid it.
A step-by-step guide to building a robust semantic layer for consistent data metrics, covering architecture, stakeholder alignment, and implementation.
Seven critical mistakes that can derail semantic layer projects in data engineering, with practical advice on how to avoid them.
Seven common data modeling mistakes that cause reporting errors and slow analytics, with practical solutions to avoid them.
A guide to choosing between batch and streaming data processing models based on actual freshness requirements and cost.
Explains dimensional modeling for analytics, covering facts, dimensions, grains, and table design for query performance.
Explains Headless BI and how a universal semantic layer centralizes metric definitions to replace tool-specific models, enabling consistent analytics.
Explains how data virtualization and a semantic layer enable querying distributed data without copying, reducing costs and improving freshness.
Explains how to safely evolve data schemas using API-like discipline to prevent breaking downstream systems like dashboards and ML pipelines.
Explains Slowly Changing Dimensions (SCD) types 1-3 for managing data history in data warehouses, with practical examples.
Explains how a self-documenting semantic layer uses AI to automate data documentation, reducing manual work and governance risks for data teams.
Explains idempotent data pipelines, patterns like partition overwrite and MERGE, and how to prevent duplicate data during retries.
Argues that data quality must be enforced at the pipeline's ingestion point, not patched in dashboards, to ensure consistent, reliable data.
