Data + Algorithm + Interpretation = Meaning
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 … Continue reading SQL Server Big Data Clusters Workshop at SQL Bits
(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 … Continue reading Syllabuck: Ignite 2018 Conference
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 … Continue reading 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 first thing you can do in your projects is to implement Infrastructure as Code … Continue reading 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 … Continue reading DevOps for Data Science – Continuous Delivery
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 … Continue reading DevOps for Data Science – Continuous Integration
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 … Continue reading DevOps for Data Science – Infrastructure as Code
