In a previous post, we had created a Spring Boot + WebSocket Hello World Example. In this post, we will be creating a real-time multi-use chat application.
In a previous post, we had also seen how to deploy Spring Boot + RabbitMQ applications to Pivotal Cloud Foundry. I have hosted the real-time chat application that we are creating to Pivotal Cloud Foundry and use can see the demo at JavaInUse Chat Application.
Containers, led by Docker, burst onto the IT scene in 2013. In the five-plus years since, container technology has reshaped the software development landscape. The shift began with a breakdown of applications into lightweight, independent, and deployable software packages.
Since each container includes everything it needs – code, runtime, system tools, and settings, plus all dependencies, libraries, and other configuration files – you can reliably move containers from one environment to another. For IT teams, this reduces frustration when problems occur due to changes between environments. It also makes the process more efficient, which benefits companies and customers.
Throughout our ten years of working with equipment manufacturers to connect, collect, and integrate operational data with enterprise systems, we’ve seen many trends impacting industrial IoT project success. The world has finally moved beyond most of the technological limitations for building innovative solutions. All the necessary tools exist to create connected product systems that perform as expected. They work. Now there’s a new trend, and it’s not a good one. We’re seeing business teams at equipment manufacturers telling engineering managers and IT leaders to evaluate and choose an IoT platform for the company. Run an online demo. Read API documentation. Build a proof of concept. Compare prices. Most of these projects never see the commercial light of day. They get stuck. Why? They get stuck because this approach to digital transformation is completely backward.
We’ve said technology isn’t the problem. Here’s the reality. The right technology for your system is available today. When used correctly by experienced teams, it will produce your desired outcomes. This is a well-charted territory. You can have remote monitoring with predictive maintenance and integrate machine data with your business workflows. These are solved challenges.
This article is dedicated to the non-commercial Java conference that I visited on September 24, 2019, in Hannover, Germany, known as Java Forum Nord. The spoken language is German but the presentations consist of diagrams and code examples so they can be understood by the non-German-speaking readers. I want to list here the lecture which I have visited with links to the presentations and also lists with headwords.
This presentation was about making development teams stronger through three topics:
Data validation is a method for checking the accuracy and quality of your data, typically performed prior to importing and processing. It can also be considered a form of data cleansing. Data validation ensures that your data is complete (no blank or null values), unique (contains distinct values that are not duplicated), and the range of values is consistent with what you expect. Often, data validation is used as a part of processes such as ETL (Extract, Transform, and Load) where you move data from a source database to a target data warehouse so that you can join it with other data for analysis. Data validation helps ensure that when you perform analysis, your results are accurate.
Determine the data to sample. If you have a large volume of data, you will probably want to validate a sample of your data rather than the entire set. You’ll need to decide what volume of data to sample, and what error rate is acceptable to ensure the success of your project.
The first Industrial revolution paved way for a new energy source — steam, which pushed industries towards mechanization. It helped industries to improve productivity.
The Second Industrial revolution introduced electricity and other innovations in infrastructure. It created new industries and opportunities to thrive on mass production lines.
As microservices become more popular, the stability between services becomes more and more important. Technologies such as flow control, fault tolerance, and system load protection are widely used in microservice systems to improve the robustness of the system and guarantee the stability of the business and to minimize system outages caused by excessive access traffic and heavy system load.
Hystrix, an open source latency and fault tolerance library of Netflix, has recently announced on its GitHub homepage that new features are no longer under development. It is recommended that developers use other open source projects that are still active. So what are the alternatives?
I used to internally cringe when people mentioned code review – where developers check each other’s work after it has been implemented and suggest improvements. The tests were in place, QA had signed off, the product owner was happy. Surely by definition code review was just the practice of looking for problems.
If that sums up how you feel about code review, then sorry — you’re going to hate this. Nowadays, I think peer review is one of the most powerful devices for projecting best practice in a product, a team, a company and beyond. Notice I call it peer review rather than code review. To me, there are some important differences. Code review is good – it raises standards and awareness of standards. But peer review is more powerful still.
What are we trying to accomplish:
For the purpose of this article, we created a simple Mule 4 project and configured mule-maven-plugin 3.1.6 for deploying to CloudHub.
