SQL Server Big Data Clusters Workshop at SQL Bits

On Thursday, 28 February 2019, I'll be teaching a brand-new course from Microsoft called "Microsoft SQL Server Big Data Clusters Architecture", which I'll be delivering as a one-day workshop at SQL Bits in Manchester in the UK. I wanted to explain how the course will work, since we'll be covering a lot of information in …

Syllabuck: Ignite 2018 Conference

(A "Syllabuck" is like a Syllabus, but more like that second definition, and certainly more random) I recently attended, presented, worked and did interviews at the Microsoft Ignite 2018 Conference in Orlando. If you have never been, you should go sometime. 30,000 people, 2.1 million square feet of space, and ten+ miles of walking per …

DevOps for Data Science – Load Testing and Auto-Scale

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The final DevOps Maturity Model  is Load Testing and Auto-Scale. Note that you want to …

DevOps for Data Science – Application Performance Monitoring

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects is to implement Infrastructure as Code …

DevOps for Data Science – Continuous Delivery

In this series on DevOps for Data Science, I’ve explained the concept of a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects is to implement Infrastructure as Code …

DevOps for Data Science – Continuous Integration

In the previous post in this series on DevOps for Data Science, I covered the first the concept in a DevOps “Maturity Model” – a list of things you can do, in order, that will set you on the path for implementing DevOps in Data Science. The first thing you can do in your projects …

DevOps for Data Science – Infrastructure as Code

In the previous post in this series on DevOps for Data Science, I explained that it’s often difficult to try and implement all of the DevOps practices and tools at one time. I introduced the concept of a “Maturity Model” – a list of things you can do, in order, that will set you on …

DevOps for Data Science – DevOps Maturity

In this series on DevOps for Data Science, I explained what DevOps is, and given you lots of resources to go learn more about it. Now we can get to the details of implementing DevOps in your Data Science Projects. Consider that the standard Software Development Lifecycle (SDLC) with Data Science algorithms or API's added in looks something like …

DevOps for Data Science – Defining DevOps

I’m wading into treacherous waters here in my series on DevOps for Data Science. Computing terms often defy explanation, especially newer ones. While “DevOps” or Developer Operations has been around for a while, it’s still not as mature a term as, say, “Relational Database Management System (RDBMS)”. That term is well known, understood, and accepted. …

The Keys to Effective Data Science Projects – Part 9: Testing and Validation

We’re continuing our discussion of the series of the Keys to Effective Data Science Projects,  this time focusing on Testing and Validating the Model. We're in the general phase in the Team Data Science Process called "Customer Acceptance". "Testing" in the general sense is the same in Data Science projects and any other typical software project - …