Data Architecture

This guide maps out the recommended reading order through the Data Architecture series and points you to the best external resources for hands-on learning at each stage. Whether you’re a complete beginner or an …

This glossary is written for business users, product managers, and data-adjacent stakeholders who need to understand what their data engineering teams are talking about. Each term includes a plain-English definition, the …

For decades, data platforms focused almost exclusively on structured data — rows and columns in relational tables. But 80–90% of enterprise data is unstructured: emails, documents, call recordings, product images, sensor …

Real-time data architecture answers a fundamental business question: “What is happening right now?” — fraud happening in the last 30 seconds, system anomalies in the last 5 minutes, inventory changes in the …

Understanding individual architectures (Lake, Warehouse, Lakehouse) is one thing — but how do you actually pattern your data flows within them? This article covers the recurring design patterns that appear across every …

After exploring each architecture in depth, the critical question becomes: how do you choose? This article provides a structured comparison and a practical decision framework to help you select the right architecture for …

Open table formats are the technological revolution at the heart of the modern data lakehouse. They transform simple object storage (ADLS, S3, GCS) — which is fundamentally immutable and lacks any concept of transactions …

Microsoft Fabric (generally available since November 2023) is Microsoft’s answer to the fragmentation of the modern data stack. Rather than stitching together Azure Synapse, Azure Data Factory, Azure Data Lake, …

Data Mesh is not just a technology choice — it is a socio-technical paradigm shift in how organisations think about data ownership, responsibility, and architecture. Coined by Zhamak Dehghani at ThoughtWorks in 2019, it …

The Data Lakehouse emerged as the architectural answer to a fundamental tension: data lakes offered unlimited scale and flexibility but lacked governance; data warehouses offered governance and performance but were rigid …