A step by step tutorial on working with Spark in a Kubernetes environment to modernize your data science ecosystem
A step by step tutorial on working with Spark in a Kubernetes environment to modernize your data science ecosystem
The notions of collaborative innovation, openness and portability are driving enterprises to embrace open source technologies. Anyone can download and install Kubernetes, Jupyter, Spark, TensorFlow and Pytorch to run machine learning applications, but making these applications enterprise grade is a whole different story.
With all the turmoil and uncertainty surrounding large Hadoop distributors in the past few weeks, many wonder what’s happening to the data framework we’ve all been working on for years?
Still waiting for ML training to be over? Tired of running experiments manually? Not sure how to reproduce results? Wasting too much of your time on devops and data wrangling?
The popularity of Kubernetes is exploding. IBM is acquiring RedHat for its commercial Kubernetes version (OpenShift) and VMware just announced that it is purchasing Heptio, a company founded by Kubernetes originators. This is a clear indication that companies are increasingly betting on Kubernetes as their multi-cloud clustering and orchestration technology. At the same time, in...
While browsing the CNCF Serverless Slack channel recently, I noticed a message; someone needed help writing a function which processes S3 update events. He didn’t want to use AWS Lambda and alternatively was looking for an open source serverless solution over Kubernetes. I took on the challenge of writing, as a response, a function for...