Businesses want speed. One of the impacts of digital transformation - and the associated pressure to succeed in an app economy - is the desire to move at speed. Nearly half (48%) of organizations are driven by digital transformation to deploy faster according to our State of Application Services 2019.
But it's not all about deployment. It's also about development and responsiveness to threats and changes in demand.
Organizations want to develop and deliver apps faster. They want to adapt to unpredictable changes in business conditions faster. They want to respond to attacks faster.
So how are they going to get it?
Deploying more frequently implies you're first developing faster. That often means adoption of speed-oriented Agile methodologies. GitLab's 2019 Global Developer Report: DevSecOps found that the majority (54%) are choosing Scrum, while 37% go for Kanban.
But methodologies aren't enough if application architectures aren't well-suited to development styles. Smaller, focused teams sprinting toward frequently releases isn't really a good fit for developing new features or fixes for traditional and monolithic applications. Componentization through the adoption of microservices and more distributed forms of architecture fit more naturally with modern architectural and operational principles. It's no surprise that, on average, more than 80% of a modern application is made up of third-party components, heavily weighted toward open source.
Adoption of APIs, as well, is no surprise amongst those seeking faster development through componentized application architectures. APIs decouple implementation from interface, and allow teams to make modifications to processing without impacting the use of APIs from other components or applications. This is a common pattern, with an impressive 64% of organizations creating APIs today for use in either internal or external use cases. Nearly 50% of those organizations rely on APIs to get ideas to market faster according to Jitterbit's State of API Integration 2018.
The build pipeline, too, has to keep up if frequently deployment goals are to be met. That means CI/CD tools that seamlessly move code from commit to test to release. The most popular CI and build tools in GitLab's survey were, unsurprisingly, GitLab (61%) followed by Jenkins (36%) and Travis CI (12%). Notably, our own research finds Jenkins in use by 16% for network automation as well - a promising result if organizations are seeking to extend DevOps beyond delivery into deployment.
Developing faster doesn't get ideas to market faster. That requires deployment. While born-in-the-cloud technology companies have mastered the delivery-deployment divide with ease, many established enterprises find this transition challenging. Existing organization structures as well as the continued need to support traditional and monolithic applications introduces conflicts that can be challenging to integrate with modern operational demands. But there is no doubt that organizations must overcome these challenges for those applications requiring faster and more frequent deployments.
Don't be fooled; established organizations are embracing automation and applying it to deployment pipeline. The issue is often that traditional IT structures introduce inconsistent automation and self-service efforts. We see this play out in our own research, where strongly siloed team structures continue to impact automation efforts across IT.
Team structures matter and organizations whose compass is pointed at automated pipelines will need to address the cultural aspects of continuous deployment if they want to consistently deploy faster.
Traditionally, the inability to achieve continuous deployment has pushed developers and app owners to turn to the public cloud, because it eliminates the obstacles in the slower, enterprise deployment path. The disparity in opinion on frequency of deployments is, in part, a contributor to the issue. Our 2018 NetOps / DevOps survey found that while 55% of DevOps and 52% of Cloud architects felt their organization did not deploy frequently enough, only 30% of NetOps and the same percentage of Operations agreed.
But it's not the only driver. Digital transformation is certainly a factor; 33% of respondents in our State of Application Services indicated they are intentionally moving to deliver apps from the public cloud as a result of embracing digital transformation initiatives. The ability to easily integrate application services and subsequently automate operation is a huge boon to organizations seeking a faster route to frequent deployment.
In the cloud - but also on-premises - continuous deployment often requires the ability to adopt per-app pipelines and application services that support the model. Increasingly, organizations are turning to containers because of their ability to support rapid updates and operate seamlessly in highly volatile environments. We see containers in demand not only to support modern application architectures like cloud-native - the top container use-case at 33% of respondents to Diamanti's 2019 Container Adoption Benchmark - but also for infrastructure. The demand for on-premises application services in containers has consistently grown year over year in our own research, rising from just 4% in 2017 to 15% in 2019.
Not everything is about delivery and deployment. Organizations also need speed when it comes to security and operations. In today's world, where more than half of all app interactions are conducted by bots, it's important for organizations to respond quickly with a 'reject' message to avoid falling prey to exploits or infection.
One of the ways organizations are seeking to speed up their ability to respond to attacks is through the adoption of real-time threat analytics. With 'security' continuing to be a top priority and consistent challenge, it was no surprise to see this category ascend into the top five strategy trends and technology in 2019 with 41% of respondents tagging it.
The availability of 'intelligent' security application services will accelerate as machine learning and automation continue to apply their considerable capabilities to the problem of identifying troublesome traffic faster.
Machine learning isn't the only way in which infrastructure and application services are getting smarter. As the need for faster responses to capacity and processing grow along with users, infrastructure and application services are evolving to include orchestration as a core capability rather than an add-on. Application service platforms with integrated orchestration layers will mean services that scale along with demand - on-demand. While such capabilities exist today - indeed, they are part and parcel of cloud and containers - the capabilities to automatically scale up and down based on application and user defined requirements is not native to most systems today. But they will be.
Finally, organizations need to combat bots for both business and security purposes. Whether organizations need to stop scraping - a real threat to business - or prevent bots from probing for vulnerabilities, identifying bad bot actors fasters is critical today. Checking a box (I'm not a robot) isn't enough today as bots are growing more intelligent and are able to foil such primitive techniques.
Organizations are turning to Bot Defense services capable of applying more modern, successful techniques to quickly identify and block bad actor bots. We've seen the use of bot defense services grow quarter over quarter in response to this need, and expect this trend to continue.
When it comes to business' need for speed today, we wind up looking at processes. Development processes are automated to gain speed with CI/CD. Deployment processes are automated to gain speed with continuous deployment and cloud. Scaling and security processes are being automated by systems and the introduction of orchestration as native to application service platforms.
Faster, it turns out, relies on an organization's ability to automate processes across development, deployment, and security.