A reference check for IT professional Richie Rump, highlighting his skills in data architecture, problem-solving, and reliability.
Explores the trade-offs between decoupled catalogs and managed tables in open table formats like Apache Iceberg, focusing on architectural freedom and operational simplicity.
Explains the fundamental divide between operational (OLTP) and analytical (OLAP) systems, the physics enforcing it, and the truth about hybrid architectures.
Analysis of LTAP (Lakehouse Transactional Analytical Processing) focusing on freshness, isolation, and workload boundaries for data architects.
Guide to implementing Model Context Protocol (MCP) in a lakehouse architecture with Python, covering specification, server building, and deployment.
Analysis of zero-copy mirroring for safer lakehouse migration, focusing on architecture, governance, and multi-engine data platforms.
Explains mathematically why traditional lakehouses fail AI agents and introduces the agentic lakehouse concept.
Explores using Lance and Iceberg formats for multimodal AI data, addressing scan-heavy analytics vs. random-access retrieval for ML training.
A comprehensive guide to data modeling, explaining its meaning, three abstraction levels, techniques, and importance for modern data systems.
A step-by-step guide to building a robust semantic layer for consistent data metrics, covering architecture, stakeholder alignment, and implementation.
Explains the difference between a metrics layer and a semantic layer in data architecture, clarifying their distinct roles and relationship.
Explains the distinct roles of data catalogs and semantic layers in data architecture, arguing they are complementary tools.
Explores the shift from traditional pull queries to using materialized views and data duplication for better performance, format, and location in data systems.
Explores implementing a data mesh architecture using dbt, outlining how dbt Mesh projects can align with data mesh principles for large-scale organizations.
A monthly roundup of 78 curated links on data engineering, architecture, AI, and tech trends, with top picks highlighted.
A comprehensive guide to the data lakehouse architecture, its core components (Iceberg, Delta, Hudi, Paimon), and the surrounding ecosystem for modern data platforms.
An introduction to data warehousing concepts, covering architecture, components, and performance optimization for analytical workloads.
Explains data lakes, their key characteristics, and how they differ from data warehouses in modern data architecture.
Explores core principles of scalable data engineering, including parallelism, minimizing data movement, and designing adaptable pipelines for growing data volumes.
Explores the modern data stack, cloud platforms, and principles for building flexible, cloud-native data engineering architectures.
