We all know we are at the peak of the hype cycle for…wait for it – Blockchain! We are also already aware of some of the benefits of blockchain - but can blockchain be applicable to traditional data management? Though real-world blockchain implementations in the enterprise are minimal so far, I do believe there is a ton of potential to solve some of the problems that businesses face.

Kubernetes provides the ability to easily deploy and run containerized applications in cloud, on-premise, or hybrid environments. Kubernetes has gained a lot of attention recently and it has become a platform for innovation in containerized applications. One technology which has probably helped Kubernetes grow a lot is Helm, which provides the means to package, install, and manage Kubernetes applications.

Few industries have been revolutionized by big data as much as healthcare. In this episode, we talked about: Gene mapping, Creating new drugs, Real-time health data, Organ donation

With increasing needs for data analytics and ever more stringent laws concerning data security and privacy (looking at you, GDPR), data governance has become a business imperative for any data-driven company. Master Data Management (MDM) is an essential part of any data governance strategy, it is commonly defined as the comprehensive method used to consistently define and manage the critical data of an organization to provide a single point of reference.

For the past few years, big data has been all the rage, but it has not delivered on all its promise. We were expecting omniscience at our fingertips, and we’re getting moderately well-targeted advertising instead. Don’t get confused though, big data is not going anywhere. On the contrary, data keeps growing at a dizzying rate, increasing in volume, variety, velocity, and veracity.

I am very excited to introduce Talend Data Streams, a brand new, cloud-native application that enables you to get streaming data integrations up and running in minutes all while providing unparalleled portability powered by Apache Beam.

With the growing adoption of cloud-based IT infrastructures, the proliferation of mobile and IoT devices, and the rise of social media, companies of all sizes, across all industries are amassing huge quantities of data in differing variety, velocity, veracity and validity.

In the past few years, there has been a shift in the data industry, leading to the emergence of a new category of data citizens: the ‘ad hoc’ or ‘citizen’ integrators. With these new personas adding to the (already long) list of data workers having access to corporate information, companies are needing to re-think the way they approach their data security and data governance strategies.

The appearance of Hadoop and its related ecosystem was like a Cambrian explosion of open source tools and frameworks to process big amounts of data. But companies who invested early in big data found some challenges. For example, they needed engineers with expert knowledge not only on distributed systems and data processing but also on Java and the related JVM-based languages and tools.

Recently, I've seen a lot of articles and conversation around DevOps. DevOps is a set of practices that automates the processes between software development and IT teams, in order to enable be shorter development cycles, increased deployment frequency, and more dependable releases all in conjunction with business needs.