The State of DevOps at the start of 2017

At the beginning of the year I contributed to an article Appdynamics put together called ‘The State of DevOps at the Start of 2017‘. I joined a motley collection of contributors with a broad range of backgrounds who all answered an interesting array of questions. If you’re unlucky enough to work in one of those organisations that’s still debating the relative merits of DevOps then I think this little article gives a nice concise summary of the current state of the industry.

Standardisation

Since I started shamelessly self-promoting my DevOps prowess I’ve been asked to contribute to a few of these things. There’s usually one question that really piques my interest and gets me thinking. In this piece it was the question:

“Do you think DevOps will become more standardized?”

Software Fragmentation

As this piece was sponsored by AppDynamics I initially started thinking about software and services. In the early days of the DevOps movement there was a real danger that it would get written off as merely Continuous Integration.  That focus spawned a plethora of software and plug-ins. Then when the #Monitoringsucks movement gained momentum it heralded a wealth of new approaches to monitoring tools and solutions, Outlyer (formerly known as Dataloop.IO) started in earnest, Datadog and the Application Performance Management tools started to really gain in popularity. Splunk and ELK become the standards for log-processing. All throughout this period various Cloud solutions launched. Redhat integrated various open-source solutions into an entire private cloud solution together which it put forward as a competitor to VMWare.

I was just putting an answer together about how at the start of major new trends in IT there’s always a profusion of software solutions around as organisations try to capitalise on all the press attention and hype when it occurred to me that the approach organisations have taken to adopt DevOps practices have become highly standardised.

Regardless of the exact mix of software and cloud solutions we always try to automate the building, testing and deployment (and more testing) of software onto automatically provisioned and configured infrastructure (with yet more testing). What has varied over the years is the precise mix of open and closed source software and the amount of in-house software and configuration required.

1st Generation

In the beginning before Patrick DuBois first coined the term DevOps Google, Facebook and Amazon all had to create automated build, configuration, deployment and test solutions to support their meteoric growth. Facebook had to create their own database software and deployment solutions. Amazon ended up building the first cloud solution to support the growth of their ecommerce site. Google had to create programming languages, frameworks and container ecosystems.

2nd Generation

The second generation of DevOps organisations including Netflix and Etsy were the first generation to be able to piece together pre-existing solutions built around CI tools like Jenkins and Teamcity to build their DevOps approaches. That’s not to say they didn’t have to build a lot of software, they did but Netflix were able to build on top of Amazon’s Cloud and Etsy relied on Jenkins and Elasticsearch among other tools. In technology circles Netflix are as famous for their codebase as they are for their video-on-demand service. While Etsy are perhaps a little less famous for their code their contributions are arguably much more influential. Where would we be without Statsd? Etsy’s Deployinator was responsible for driving a lot of the conversation about continuous integration in the early days of DevOps.

3rd Generation

I think we’re getting towards the end of the 3rd generation of DevOps. The current approach to abstracts us even further away from the hardware. It’s built on the concepts of containerisation, microservices and serverless functions. We’re trying to build immutable services, very rapidly deployed and automatically orchestrated. The goal is to support the most rapid test and deployment cycle possible, to minimise the need for separate functional and integration test cycles, to make the most efficient use of resources and allow us to scale very rapidly to meet burst demand. The software we’re forced to build at this point tends to be integration software to make one solution work with another and expose the results for analysis. Rather than building our solutions around CI tools we’re building them around orchestration mechanisms. We still need the CI tools but their capabilities aren’t the limiting factor anymore.

4th Generation

I think the next generation of DevOps will bring data management and presentation under control. Too often we choose the wrong data stores for the types of data we’re storing and the access patterns. There’s still a tendency to confuse production data stores with business intelligence use-cases. We’re still putting too much business logic in databases forcing us to run large data structure updates and migrations. We also need to lose the idea that storage is expensive and that everything must be immediately consistent, we also need to manage data like we do software in smaller independant files.

Standards

From the perspective of the daily lives of engineers and their workflows DevOps appears highly standardised. We haven’t standardised around a small number of software solutions that define how we work as previous generations of engineers did we have standardised our workflow. We’ve been able to do that thanks to the open source movement which has given us so many flexible solutions and made it easier for us to contribute to those solutions.

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