It often makes business sense to code microservices, customized applications, innovative customer experiences, enterprise workflows, and proprietary databases. But there are also times when the business and technology teams should consider low-code and no-code platforms to accelerate development, provide out-of-the-box technical best practices, simplify devops, and support ongoing enhancements.
Low-code platforms come in several categories. Some focus on tools for rapidly developing web and mobile user interfaces and workflows. Many data visualization, data integration, and data prep tools are low code, and emerging low-code platforms support machine learning, Internet of Things (IoT), and IT automations.
The cloud is typically a destination for systems needing to be modernized to take advantage of technologies such as AI, predictive analytics, or a hundred other cloud services. It’s typically cheaper, it can be allocated and changed in minutes, and the enterprises technology elites are spending most R&D dollars on the public cloud these days. Thus, your existing platforms are no longer getting the love.
Moving to the cloud is not a bad idea. However, the trouble comes when enterprises believe that digital enablement will somehow fix existing problems, such as a data mess, application issues, inadequate security, or frequent outages due to a lack of operational disciplines and tools.
Big companies such as General Electric, Siemens, and Robert Bosch are using edge computing technology to optimize production. Manufacturing is a large consumer of edge approaches and technology.
Typically, these edge systems are powered by artificial intelligence (AI) systems that parse production data at the source of the data. This enables them to make instantaneous decisions, such as adjusting the cooling systems in a factory so the welding robots can make more precise welds.[ Also on InfoWorld: Amazon, Google, and Microsoft take their clouds to the edge ]
Although the value of edge computing is well understood at this point, it’s still being used to solve problems where it’s not needed. We’re back to ensuring that whatever technology is being hyped right now is still a good choice.
The only time I had an issue with someone I worked for was when they wanted me to punish a junior IT architect on my staff for making a pretty big mistake. One of the databases was not compatible with a middleware layer already in existence.
Obviously, this error cost us time and money. But these kinds of mistakes are almost unavoidable when configuring IT systems, cloud computing included. I view them as necessary in the innovation process. If you don’t try new things—and find out some of them don’t work—then you’re not improving anything. I encouraged my boss to find a new line of work, and eventually, he did.[ Also on InfoWorld: When hybrid multicloud has technical advantages ]
So, if mistakes are a natural byproduct of creating a good and innovative new architecture, then it’s time to look at the mistakes that are made most often. For cloud architectures, those mistakes should be understood by now and avoided. Here are three that come to mind: