The Java Message Service API (JMS) was developed by Sun Microsystems in the days of Java EE. The JMS API provides us with simple messaging abstractions including Message Producer, Message Consumer, etc. Messaging APIs let us place a message on a “queue” and consume messages placed into said queue. This is immensely useful for high throughput systems – instead of wasting user time by performing a slow operation in real-time, an enterprise application can send a message.

Mattermost v6.6 is generally available today and includes the following new features (see changelog for more details).

Confidence in the quality, robustness, and reliability of a product are among the most valuable qualities sought after by consumers as well as businesses. This confidence is built through the rigorous testing of a product. In software engineering, practices like extreme programming (XP) and test-driven development (TDD) champion the belief that automated testing should be used from the start of a project.

I was intrigued by two recent IoT related survey and reports. They emphasize use cases about efficiencies and return on investments but, surprisingly, there is no mention of people-safety nor productivity. I think it is imperative to place people at the heart of the IoT universe. Today, people related use cases have been greatly understated. We will be doing a great injustice if we continue to ignore the human angle.

Developers strive to produce efficient code. Many times, developers will add code to their repositories and test it to make sure it works, but they are forgetting one very important step: benchmarking! Benchmarking allows developers to see the performance impact on their code output. If properly integrated into a CI/CD pipeline, it could prevent catastrophic drops in performance before any code is shipped/deployed at all.

In order to answer this question, it is necessary to define what middleware is. It is generally accepted that middleware is software that sits between existing systems and processes and helps to smooth integration and interaction between them. Because of the sheer range of middleware and the scope of what it can do and affect in terms of business infrastructure, it is vital that it can be successfully monitored and managed.

Apache Kafka is a very popular open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Amazon MSK (Managed Streaming for Kafka) is a fully managed Kafka service that makes using Kafka even easier. But how do you get started with Kafka on AWS?

Apache Kafka is a very popular open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Amazon MSK is a fully managed Kafka service that makes using Kafka even easier. But how do you get started with Kafka on AWS? How do you migrate your existing Kafka environment to the cloud? How do you manage many Kafka environments at once?

Who’s faking it? Data-driven decision-making (and ensuring related investment is delivering on its promises) has been elevated in importance. Every enterprise is replacing opinion with data-driven decision-making, correct? A brand new research report from EMA (Enterprise Management Associates) titled, “A Data-Driven Enterprise” looks behind the curtain to separate facts and reality from fiction and aspiration. Here are some of the findings from the latest research on enterprises.