The trend has been clear for years and still pertains: innovative app-driven customer experiences are disrupting any market that software touches. As development teams are now central to driving business, microservices architectures and DevOps practices are fundamental in continuously innovating what apps enables customers to do.
The cloud native development model is an engine for innovating customer experience, providing the way to continuously integrate improvements. This model includes three essential components: A microservices application architecture, in which business function is divided among independent but interoperable code modules; DevOps methods, practices, and tools for teams to continuously collaborate on and deliver improved experiences to end users; and a container-based cloud platform in which to do this work.
But cloud platforms themselves continue to rapidly evolve. For example, depending on business purposes, container orchestration systems and serverless technologies are both viable options for creating and publishing customer-facing apps.
Additionally, many teams bear the pressure to innovate while also transitioning to the cloud native paradigm from IT systems they’ve been building and maintaining for the past decade–using mainframes and application servers, for example. In this situation, App Dev leaders must figure out what existing apps to keep on premises but extend through API-driven cloud services, what apps to modernize by moving an existing monolith onto a cloud platform and creating the right new microservices to extend its value for customers. Modernizing apps is innovating
Streamlining cloud-based app development is especially valuable in modernizing applications. An app development service that focuses developers new to cloud native provides obvious benefits related to kick-starting innovation.
With that in mind, the IBM Cloud App Service provides starter kits—choice of coding languages, app frameworks/patterns, and DevOps toolchains that automate the necessary stages from initial coding to production pushes after successful testing.
Within minutes, developers setup an environment based on their preferred languages, the application framework that fits their objective, a code repository that can be easily cloned to enable more efficient coding locally, provisioned Cloud Foundry or Kubernetes runtime environments, and stages in an automated DevOps pipeline.
Developers can continuously commit code to a cloned local repository mutliple times a day. When ready, they push code to a mirror repository on cloud platform, automatically kicking off app build and deployment to either a relevant Cloud Foundry runtime or Kubernetes cluster for testing. Whether developers use command line from within IBM Cloud or from within a local IDE, the App Services provides the same experience.
As a result, by modernizing valuable existing apps, and making a bigger transition into cloud native, teams gain the ability to sustain focus on innovating.
Take the Cloud Native Developer Challenge
I’ll end with what you can do in 5 minutes: take some code you esteem and already works, and deploy it to IBM Cloud with these steps. You’ll get a working microservices app within a DevOps framework that puts the existing app on a fast track for the next functional innovation that end users will care about.
IBM Log Analysis with LogDNA is available today on IBM Cloud. This service to simplifies log management in the Cloud and helps your developers pinpoint issues quickly in their dynamically scaling applications and workloads.
Starting in December of 2018, IBM Cloud DevOps Insights will change the way it charges for usage. Instead of charging by Application, Insights will use the term Item. By charging for items within Insights, we will be able to scale our value across future feature sets.
IBM Netcool Operations Insight on IBM Cloud Private is a containerized version of Netcool that covers operations management and agile service management. You can now run your management software on the same modern platform as other workloads, making it cost-effective and scalable for future migrations.