22-25 April 2026

Deciphering Data Architectures full-day workshop

Proposed session for SQLBits 2026

TL; DR

This workshop defines big data and core architecture concepts, then explains warehouses, lakes, marts, virtualization, ETL vs. ELT, and compares Modern Data Warehouse, Data Fabric, Lakehouse, and Data Mesh, highlighting pros, cons, and practical considerations.

Session Details

This pre-conference workshop will begin by defining 'big data' and clarifying various data architecture concepts to establish a solid foundation of understanding before delving into specific data architectures. Topics to be covered include relational data warehouses, data lakes, data marts, data virtualization, and the differences between ETL and ELT. James will then explore and compare the architectures of the Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh in considerable detail, highlighting their advantages and disadvantages. While these concepts may seem appealing in theory, James will address potential concerns to consider before implementation. This workshop aims to demystify these complex topics, offering ample opportunity for questions. The content is derived from James's book "Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh."

3 things you'll get out of this session

Provide a foundational understanding of big data and core data architecture concepts. Explain and compare major architectures—Modern Data Warehouse, Data Fabric, Lakehouse, and Data Mesh. Equip attendees to evaluate pros, cons, and implementation considerations for each approach.

Speakers

Paul Andrew

mrpaulandrew.com

Paul Andrew's previous sessions

An Evolution of Data Architectures - Lambda, Kappa, Delta, Mesh & Fabric
How has advancements in highly scalable cloud technology influenced the design principals we apply when building data platform solutions?
 
Building an Azure Data Analytics Platform End-to-End
Based on real world experience let’s think about just how far the breadth of our knowledge now needs to reach when starting from nothing and building a complete Microsoft Azure Data Analytics solution.
 
Creating a Metadata Driven Orchestration Framework Using Azure Data Integration Pipelines
We'll explore delivering this framework within an enterprise and consider an architect’s perspective on a wider platform of ingestion/transformation workloads with multiple batches and execution stages.
 
ETL in Azure Made Easy with Data Factory Data Flows
What happens when you combine a cloud orchestration service with a Spark cluster?! The answer is a feature rich, graphical, scalable data flow environment to rival any ETL tech we’ve previously had available in Azure.
 
Using Azure DevOps for Azure Data Factory
DevOps as a concept does not always translate to the technology when implemented. In this session we'll explore that problem when working with Azure Data Factory and what the different cloud only CI/CD options are.
 
Complex Azure Orchestration w Dynamic Data Factory Pipelines
If you have already mastered the basics of Azure Data Factory (ADF) and are now looking to advance your knowledge of the tool this is the session for you.
 
Building an End to End IoT Solution Using Pi Sensors & Azure
Demonstrating an end to end IoT solution providing real-time sensor data from a Raspberry Pi into an Azure IoT Hub, through Stream Analytics, then with outputs to Power BI and SQL DB. Learn how to build this simplified IoT solution from scratch.