The overall importance of data and information within organizations has continued to grow. We’ve also seen the continued rise of megatrends like IoT, big data – even too much data – and of course, machine learning. That’s along with the…

The England and Wales Cricket Board governs every aspect of the sport in those two countries, and it holds massive amounts of data generated by professional and amateur players alike. Since its founding in 1997, the ECB has collected information…

Google BigQuery is a serverless, highly scalable, low-cost enterprise data warehouse that helps data analysts become more productive. It enables extremely fast analytics on a petabyte scale. And its pay-as-you-go model makes it attractive for organizations looking to move away…

Percona Live is always a great opportunity to learn from the best and brightest in the open-source database community. This time, Percona Live Europe is being held at the Hilton Amsterdam Airport Schiphol, The Netherlands from September 30 to October…

As a solutions architect at Pythian, I often get questions from clients about the many solutions available to them to address their big data needs. Between Hadoop, cloud-based, and hybrid solutions, finding the best option for their unique needs can…

I’ve been interviewing and mentoring candidates for technical database positions for almost ten years, and a common question from those who either don’t make it or feel they’re not ready yet to apply to the position they’re looking for is:…

Unisphere Research Report uncovers the trends that are driving analytics workloads to the cloud, including real-time analytics and machine learning.   Data strategies are now commonplace, machine learning adoption has doubled in a year, and data warehouses are still a go-to…

Many businesses migrate to the cloud to take advantage of cost savings and improved agility. Optiva, a Canada-based company that develops software for communications service providers, recently migrated to Google Cloud Platform (GCP) with help from Pythian’s team of experts. Since…

The first step to kick off a Machine Learning (ML) project is to have a written proposition for the business problem, and second, to frame the ML problem. Before even discussing an ML method, it is necessary first to understand…

The difference between data science consultants and a data science team at a company depending on machine learning (ML) solutions is significant. In the first instance, the consultants need to have a good understanding of both the business and technology….