Readers of this blog have probably read about allegro.ai’s partnership with NetApp. Earlier this year the two companies showcased an integrated AI solution at the NVIDIA GPU Technology Conference (GTC) which took place in San Jose, California.

This article follows our first blog post related to Kraken’s deployment on Kubernetes. It is a step by step guide explaining how to deploy the InfluxDb/Telegraf/Grafana stack used to generate load testing reports on Kraken.

We are thrilled to announce the release of Kong Ingress Controller 0.6! This release builds on the previous releases and unlocks integrations and features, including the Admission Controller, integration with Kuma and Istio, and support for Kustomize native configuration management.

Today, 90% of enterprise analytics and business professionals say that data and analytics are key to their organization’s digital transformation initiatives. However, there are many companies that are reluctant to pull the trigger because they aren’t sure about the advantages of becoming data-driven. In this article, we’ll look at 5 statistics that show how becoming a data-driven business can help you outdo your competition.

One of the most popular business buzz words du jour is ‘frictionless’. The term has been co-opted in recent years to describe the phenomenon of commercial processes which appear to work smoothly and effortlessly – on the customer side at least.

Over the past several weeks, the term DataOps has reverberated throughout the walls of Hitachi Vantara offices around the world and soon it will take the spotlight at NEXT 2019, the world’s first smart conference. We’ve defined DataOps and how its changing the game by putting the right data into the hands of teams, when and where they need it most. We’ve also talked about Hitachi Vantara’s DataOps journey, Project Champagne.

In this blog post I’m going to write about a famous piece in visualization history. How can we prove that a visualization is more worth than just looking at the data? That’s a question Francis Anscombe probably asked himself when he back in 1973 constructed the dataset that became known as Anscombe's quartet. A dataset he could use to show statisticians how wrong they were thinking that “numerical calculations are exact, but graphs are rough."

Businesses are collecting massive amounts of data as a part of their analytics pipeline. Most of the time, this data is filtered by a computer and presented in a way that a human interprets, through the analytics dashboard. That's a fantastic resource, and has no doubt been of great value to you in business decisions. However, restricting the interpretation of all that data that you've mined to humans leaves a lot of potential insights on the table.