James Hughes, VP of Sales Engineering, Enterprise CTO, Rubrik
“You must consider the operations side, especially for business-critical applications, because it falls on the DevOps team to keep everything running.”
A major obstacle to speedy application development, especially in large organizations, is getting the project off the ground—getting funding, bringing in the right people, and getting stakeholders to agree on the problem and how you’re going to solve it.
A key success factor in getting something out the door quickly is the operating model. The operating model is governed in part by the technology platform but also by the way the technologies and development process are organized. The fastest way to get things done is to create an agile framework that all your developers and infrastructure people use for their work. An agile process enables you to create a minimum viable product that can be in production
while you iteratively add capabilities.
To do this, you need a solution architecture that provides as uniform and repeatable a process as possible. Then, you can build reusable microservices that become building blocks for your application. Microservices make application development more flexible, but they also present challenges. For
instance, if all your microservices are written in the same language using the same application programming interfaces (APIs) and run in the same cloud, that simplifies application development but can create limitations because microservices are designed to be much more flexible than that. If
you’ve got many microservices written in different languages running in different clouds and data centers that are linked only through APIs, scaling such a solution can be more challenging.
When planning for application scalability, consider technology costs associated with scaling, such as processing capacity, licensing fees, and bandwidth. You can optimize these aspects of application scaling through automation tools. Beyond that, you must consider the operations side, especially for business-critical applications, because it falls on the DevOps team to keep everything running.
In DevOps, everyone loves the “dev,” but no one loves the “ops.” For efficient application delivery, build a proper scale framework on the operating platform all your developers and infrastructure people will be working on. With that central application director and proper application design, you can scale and automate everything, which is why machine learning has become so important to the operations side. Massive systems running at scale generate a lot of noise in terms of logs and data. When you have five problems on your system generating a million alerts, you need algorithmic information technology (IT) operations to resolve them. You must consider scalability from the beginning of application design.
