Chris Taylor

Information & Communications Technology

SQL Server Azure DevOps Azure Data Platform

Newcastle upon Tyne, England, United Kingdom

Chris Taylor


Chris (@SQLGeordie) is a UK Based Data Platform Architect for Data Masterminds providing SQL Server, Microsoft Azure and AWS consultancy to clients worldwide in a variety of industries. He loves all areas of SQL Server and specialises in Security but really gets his kicks from Automation and DevOps.

He is a frequent speaker at both local and international data events, the founder of the Newcastle Data Platform and Cloud (DPaC) and Newcastle Power BI meetups.

Current sessions

Database CI/CD with Docker Containers and Azure DevOps

Containers are becoming ever popular but other than spinning one up to have a play around with the latest SQL Server 2019 new features, what else can they be used for?

In this demo heavy session we will show how Containers along with Azure DevOps can work for your CI/CD pipeline.

You will leave this session with the knowledge and ability to start creating your own bespoke SQL Server on Linux development / testing environments with automated image builds and releasing to Azure Kubernetes Service (AKS) along with some of the issues / pitfalls of doing so.

To get the most out of this session a basic understanding of what Docker/Containers are, is recommended.
The classic editor will be used to help visualise what is being done, YAML version will be made available.

"Kubernetify" your Containers

We have all now had a play around with Docker and Containers or at least heard about them.

This demo heavy session will walk through some of the challenges around managing container environments and how Kubernetes orchestration can help alleviate some of the pain points.

We will be talking about what Kubernetes is and how it works and through the use of demos we will:

- Highlight some of the issues with getting setup
- Deploying/Updating containers in Kubernetes (on-Prem as well as AKS using Azure DevOps)
- Persisting your SQL Server data
- How to avoid making the same mistakes as I have