A Data Engineer’s Guide to Azure SQL Data Warehouse

Azure SQL Data Warehouse, Microsoft’s scale out database engine for SQL analytics in the cloud, provides a blazing fast, petabyte-scale system that can handle your most demanding analytical workloads with ease. SQL Data Warehouse takes advantage of the latest Azure hardware technology delivering up to 100x performance boosts on customer workloads. In short, Azure SQL Data Warehouse is a SQL analytics beast!

We have built this all-day pre-con to help you, the data engineer, make the most of all this power that is now at your disposal!

We’ll be bringing you in-depth technical knowledge combined with hands-on implementation experience to help you get the most from the scale out architecture and accelerate the delivery of your projects. We’ll be arming you with theoretical knowledge, hands-on knowledge, best practices, hints and scripts to ensure you leave this training with all the tools you need to be successful.


The day will be broken down into the following modules:

  • Service Architecture -Discussing the engine itself, core concepts and the basics of using the service
  • Designing Tables -  Best practice data modelling using distributions, partitioning and performance patterns
  • Loading Data - Patterns for different data loading scenarios and how to optimise them
  • Querying Data - Deep dive into query performance, execution plans & how to avoid common performance pitfalls
  • Architecture Patterns - Integration techniques with other Azure components to build SQLDW into a complete architecture

Learn directly from the Microsoft Product group and industry leading consultants. Get the best of both worlds!

This pre-con will be delivered by James-Rowland Jones, Principal Program Manager at Microsoft for Azure SQL Data Warehouse, in partnership with Simon Whiteley & Terry McCann from Adatis Consulting. Simon and Terry have delivered several large-scale,real-world SQLDW projects for some of the UK’s largest Azure users to give you additional real world insights that can only be gained through extensive industry experience.

A prior knowledge of traditional data warehousing is assumed, but not strictly necessary
Wednesday 21 February 2018