Reactive programming is a programming technique for asynchronous applications that lets you structure your code based on “reaction” to data input changes instead of an imperative programming style where you have to poll or block and wait for changes to happen. If you’re not 100% familiar with ReactiveX (RxJava being the implementation for the JVM), perhaps you know Java Stream, which is a similar concept introduced in Java 8.

Meet Dominika Dronska, head of Digital at Abbey Road Studios where the Beatles recorded almost all their albums. Using Flutter, Abbey Road Studios have built the award-winning Topline app that lets musicians record their ideas on the go and share them with each other.

A household name in global media analytics – let’s call them MTI – is using Unravel to support their data operations (DataOps) on Amazon EMR to establish and protect their internal service level agreements (SLAs) and get the most out of their Spark applications and pipelines. Amazon EMR was an easy choice for MTI as the platform to run all their analytics. To start with, getting up and running is simple. There is nothing to install, no configuration required etc.

The introduction of AI, automation and data storytelling to the world of analytics has not only had an immediate impact on the end users of analytics but also the people that work in the field. While many analysts may fear they will be replaced by automation and AI, CEO of Yellowfin, Glen Rabie, believes that the role of the data analyst will increase in significance to the business and breadth of skills required.

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.

To kick off our series on debugging for software developers, we tell you how to build breakpoints to isolate key information about your app’s performance, and save crucial time during the process.

As API programs gain traction, we know many companies want to empower developers to quickly build and deliver their API products. To aid them in this effort, we recently announced the availability of new capabilities in Apigee, the enterprise API management platform of Google Cloud Platform (GCP), to help enterprise IT teams speed up their API development. With faster API development within GCP, you can innovate faster and create connected customer experiences, plus increase developer productivity.

In the last three parts of this four-part series, we have looked at: research on the state of analytics today and the lack of BI adoption; the history of BI and how we have arrived at the augmented era; and the four main blockers to BI adoption that is stunting the growth your business data culture. Today, let's take a look at how AI and machine learning (ML) can close that adoption gap.