Pub/sub messaging: Apache Kafka vs. Apache Pulsar

These days, massively scalable pub/sub messaging is virtually synonymous with Apache Kafka. Apache Kafka continues to be the rock-solid, open-source, go-to choice for distributed streaming applications, whether you’re adding something like Apache Storm or Apache Spark for processing or using the processing tools provided by Apache Kafka itself. But Kafka isn’t the only game in town.… Continue reading Pub/sub messaging: Apache Kafka vs. Apache Pulsar

IBM preps Watson AI services to run on Kubernetes

Two of IBM’s Watson-branded collection of machine-intelligence services will be available to run as standalone applications in the public or private cloud of your choice. IBM is delivering these local Watson services atop IBM Cloud Private for Data, a combined analytics and data governance platform that can be deployed on Kubernetes.  Ruchir Puri, CTO and chief architect… Continue reading IBM preps Watson AI services to run on Kubernetes

How to use Azure Data Explorer for large-scale data analysis

One of the big issues facing anyone building a data-driven devops practice is, quite simply, the scale of the data you’re collecting. Logs from millions of users quickly add up, and the same is true of the internet of things or any other large source of data. It’s a world where you’re generating terabytes of… Continue reading How to use Azure Data Explorer for large-scale data analysis

Tutorial: Spark application architecture and clusters

Before you begin your journey as an Apache Spark programmer, you should have a solid understanding of the Spark application architecture and how applications are executed on a Spark cluster. This article closely examines the components of a Spark application, looks at how these components work together, and looks at how Spark applications run on… Continue reading Tutorial: Spark application architecture and clusters

Review: MXNet deep learning shines with Gluon

When I reviewed MXNet v0.7 in 2016, I felt that it was a promising deep learning framework with excellent scalability (nearly linear on GPU clusters), good auto-differentiation, and state-of-the-art support for CUDA GPUs. I also felt that it needed work on its documentation and tutorials, and needed a lot more examples in its model zoo.… Continue reading Review: MXNet deep learning shines with Gluon