In Kong Mesh 1.2, we added a number of new features to help enterprises accelerate their service mesh adoption. One of the major new features was native Open Policy Agent (OPA) support within the product. In the demo image above, you can see a number actions taking place across a simple web application. These “actions” ultimately are various GET, POST, and DELETE methods (API calls) across various tiers of our microservice application.

While a vital component of any app's stack is the database, managing a database is challenging and hard work. Not only do you need to set up, maintain, scale, and patch databases, but also you need to create strong backup policies, ensure sharding and replications. Long story short, managing a database is time-consuming and requires a dedicated and skilled team, which is why a major trend for managing databases is DBaaS, or Database as a Service.

On the 14th of January 2021, Elasticsearch B.V. announced that future releases of Elasticsearch and Kibana would be released under a dual license SSPL (Server Side Public License). As a result of this change it is evident that the components that make up Elasticsearch and Kibana in version 7.11 (and onwards) of the ELK Stack will no longer be considered as open source based upon the Open Source Initiative's requirements for licensing.

The success of data-driven initiatives for enterprise organizations depends largely on the quality of data available for analysis. This axiom can be summarized simply as garbage in, garbage out: low-quality data that is inaccurate, inconsistent, or incomplete often results in low-validity data analytics that can lead to poor business decision-making.

The faster you can extract, transform, and load data from MongoDB, the better it is for your business processes and business intelligence systems. The problem is, most ETL solutions struggle to manage MongoDB’s dynamic schemas, NoSQL support, and JSON data types. That’s not the case with Xplenty – which was optimized for easy, no-fuss MongoDB integrations with ease: no custom code, no delays, no confusion.

Understandably, however, the many automation, AI and machine learning technologies that come with modern analytics solutions can sometimes be hard to keep up with. One area we get asked a lot about is ABM, which we offer with Yellowfin Signals, and what exact advantages it brings to the table for everyday analysis and insight generation.

The exponential adoption of IT technologies over the past several decades has had a profound impact on organizations of all sizes. Whether it is a small, medium, or large enterprise, the need to create web applications while managing an extensive set of data effectively is high on every CIO’s priority list. As a result, there has been an ongoing effort to implement better approaches to software development, data analysis, and data management.

Companies deploying big data analytics to gain competitive advantage can quickly sour their successes by lacking a big data governance strategy. Which turns their data assets into data liabilities. In this article, we dive into the field of information governance and information management and explore how to set up and automate a big data governance program for success. Big data governance is a set of processes and principles that ensure the high value of data throughout its lifecycle.

For some time now, I’ve been trying to combine Fable and WebRTC in my daily work. In this article I am going to describe the process of implementing a signaling mechanism for WebRTC.

As a developer, you're no stranger to your vast and varied data environment… Or are you? The tremendous amount of data your organization collects is stored in various sources and formats. You need a way to understand where and what data is, to be able to do what you need to do: build amazing event-driven applications.