In part 1 of my post on data governance fundamentals, I introduced the "5 Ws and 1 H" of problem-solving—"What”, “Why”, “Who”, “When”, “Where”, and “How”— and applied the first three to data governance. This part covers how you can apply the last three pieces and suggest some next steps. Let's get started!
Jumping to conclusions has always been considered a bad way to do business (and most things in life). It implies making a rash, poorly considered decision without the facts and without an understanding of the wider implications associated with that decision. But there’s a new wave of technology that is changing everything.
Recently, I read an article by Jill Dyché which was a wrap-up of TDWI Las Vegas. She talked about speaking to an analytics professional who works for a bank and was building analytics on top of their applications. This comment really struck me because it means the bank’s software vendor is missing out on a great opportunity to create an enormous amount of value for their customer and their own business.
In October 2018, we launched two new products into the Yellowfin Suite: Signals, an automated discovery product that discovers critical changes in your data as they happen, and Stories, a data storytelling product which enables users to provide better context to the numbers and create a common, consistent understanding across the organization. What did we do next? Drink our own champagne, of course.
In my last post I laid out our reasoning for moving from Heroku to Google Kubernetes Engine (GKE) and other GCP services. Now I'll describe the actual migration process in detail. This isn't designed as a how-to guide for migrating from Heroku to GKE—Google has their own excellent tutorial for that—but rather a description of some of the challenges of migrating real-world production applications and how we overcame them.
Ever since we started logging with Bugfender back in 2015, we’ve been working towards integration with Firebase, the app development platform created by Google. Firebase is famous for the breadth of its integration libraries and millions of people use the product around the world, drawn to its sleek UI and range of features.
We recently launched a new logo. Here’s why we did it and the thought that went into the design.
Navigating the transition from monolith to microservices can be an ambitious undertaking for any organization. Today, we’re launching our new Kong GTM Partner Program to create a global ecosystem of experts to help organizations successfully transition to microservices, service mesh and other modern architectures.
Today during Microsoft Build 2019, we announced that Talend Cloud, our Integration Platform as a Service (iPaaS), would soon be available on Microsoft Azure starting in Q3 2019. For those who have selected Azure as their cloud platform of choice, Talend Cloud running natively on Azure will provide enhanced connectivity options for modern data integration needs as well as better performance.
We’ve made a lot of changes to our Test Writing Service over the past few months and it’s become the powerhouse of test creation for many Rainforest users. In this post, we’ll dive deep into how Rainforest Test Writing Service works, and how you can use it to scale up and streamline your test case management workflow.
