Multi-Cloud & Kubernetes: Cloud Academy November 2018 Data Report

https://cloudacademy.com/research/multi-cloud-kubernetes-devops-cloud-academy-data-report-nov-18/

November 2018 | Cloud Academy Data Report No. 1

In the inaugural Cloud Academy data report, we have some interesting data to share around the multi-cloud and Kubernetes that might validate your perceptions of shifts in the cloud computing industry. Before we get to the juicy stuff, let’s start by discussing our mission with these data reports. Cloud Academy is the digital skills management platform that makes customized technical training manageable and measurable at scale. We deliver powerful solutions that enable our customers to align teams with important milestones on their transformation roadmaps and beyond. We’re enthusiastically making things like Cloud Roster, Cloud Catalog, and this research publicly available because we’re passionate about cloud computing and in a position to:

• democratize our aggregated and anonymized data – and any insights we can glean from it – to the broader cloud computing community
• empower organizations to make more intelligent decisions about the job roles they define as critical to creating a culture of collaboration and innovation
• help individuals understand what the market is expecting from a skills perspective and focus training efforts accordingly
• inspire discussion, interpretations, and hypotheses around data (if you have ideas we haven’t considered, please share them directly  or find us on Twitter @cloudacademy).

About the Data in this Report

Cloud Academy collects and analyzes upwards of 3,000 job descriptions each day for several cloud job roles based in the United States (we do have plans to expand). The data is then de-duplicated and analyzed. The technical job roles for which we collect job postings are: Cloud Architect, DevOps Engineer, Full-Stack Engineer, Network Architect, Security Engineer, QA Engineer, and Data Engineer. This data report in particular leverages our job posts data warehouse and will reference measurements like:

Skill Relevancy: this is the association of a given technical skill to a job role based on the existence of a technology’s mention in a job post.
Skill Proximity: this is the frequency with which two or more skills appear within a category of job posts during a particular timeframe.

Proof that the World is Officially Multi-Cloud

Just 12 months ago, the concept of “multi-cloud” still seemed like mostly marketing hype. We even published a white paper that tried to separate fact from fiction. At AWS Summits this year, we heard lots of whispering that companies were using Azure for a subset of their applications and workloads. At Microsoft Ignite in September, people were very eager to share their initiatives by platform and most included both Azure and AWS. The anecdotes, which indicated that the world is indeed multi-cloud, seem to be backed up by data.

Let’s start to unpack the data around multi-cloud by looking at which combinations of public cloud platforms DevOps practitioners are being asked to have sufficient familiarity with.

What cloud platforms are DevOps professionals being asked to understand?

DevOps is in and of itself a mindset. Some argue that there is no such thing as a DevOps Engineer. Despite this, the role exists. The DevOps Engineer job role is one of the most sought-after roles in the United States cloud job market. The measure of skill relevancy for DevOps is generally a good indication of what technical decisions organizations have been made and what technologies organizations are using.

So what cloud platforms are DevOps professionals being asked to understand proficiently? The answer is no longer just AWS, and the answer seems to be changing rapidly.

AWS, Azure, and Google Cloud Skill Proximity – Data from a Quarter Ago

AWS, Azure, and Google Cloud Skill Proximity – Current Data

In our most recent data set, of those DevOps job posts that mention AWS, 42% also mention Azure. This figure has consistently trended upward over the past couple quarters.  A mere quarter ago, that figure was 28%. Either all skills on AWS and Azure are perfectly interchangeable (some are in concept, but this simply is not the case) or more likely the multi-cloud is here to stay and it is comprised primarily of two public clouds: AWS and Azure.  Google Cloud Platform (GCP) is mentioned in only 7% of the job postings that mention AWS and in 15% of those that mention Azure.

The data indicates that organizations seeking Azure talent typically also look for people familiar with AWS. Of job posts mentioning Azure, 79% also mention AWS (up 9% from two quarters ago). This seems to support the observation that most companies went all-in on AWS initially and have been increasingly looking to Azure for certain workloads or to support certain teams.

That leaves GCP firmly in third place. Very few companies seem to be seeking DevOps professionals who are skilled with GCP and not AWS or Azure as well. For each of the past six weeks, over 90% of job posts that mention GCP also mention AWS. During the same time period, over 60% of job posts that mention GCP also mention Azure.

Companies Aren’t Necessarily Buying the Argument that GCP is the Best Fit for Data Engineering

We’ve often heard that companies are using GCP because of the best-fit-for-data-science argument. We inspected Data Engineering job posts over the same time period. GCP didn’t crack the top 20 technical skills list for the job role. Both AWS and Azure did. You can view the entire current stack ranking and filter by job role here.

We’ll keep our eyes on multi-cloud trends over the coming months and quarters. For now, saying “we’re multi-cloud” safely means “we use AWS and Azure.”

For now, saying that 'we are multi-cloud' safely means 'we use AWS and Azure.'CLICK TO TWEET

Kubernetes May be Driving Multi-Cloud

While architecting a single application across multiple public clouds is likely still not a common practical use case, flexibly shifting applications and workloads from one public cloud to another has become much easier thanks to the rise of containerization. So what role does the leading modern container orchestration technology, Kubernetes, play in a company’s willingness to shift across and between the leading public clouds?

First, let’s look at Kubernetes’ usage in conjunction with the leading three public cloud providers in the US. Examining the Cloud Architect data set, we can see how frequently AWS, Azure, or GCP are mentioned in posts that lead with K8s.

Usage of Kubernetes and AWS, Azure, or Google Cloud – Current Data

We wanted to understand the proximity of K8s to the use of two or more public clouds. To achieve this, we segmented all Cloud Architect job posts into three categories based on an indication of use of exactly one, two, or three public cloud providers. We then segmented those posts into two buckets: one where K8s was a required skill for the role and another where it was not. We found a correlation; as the number of public clouds mentioned in a given Cloud Architect job post increases, the likelihood that K8s is required also increases. Of those Cloud Architect job posts that reference one cloud, just 9% mention K8s. The figure increases to 15% and 33% for two clouds and three clouds, respectively.

We didn’t want to limit our analysis to Cloud Architects, so we ran the same analysis on our DevOps Engineer data set. The trend we found is very similar. Of DevOps Engineer job posts where just one cloud is mentioned, K8s is found 21% of the time. For those where two clouds are mentioned, K8s is mentioned 30% of the time. Finally, in DevOps job posts where AWS, Azure, and Google Cloud Platform were mentioned, Kubernetes is referenced 37% of the time.

We don’t want to confuse correlation with causation here. It is very likely that for some organizations, the use of multiple public cloud providers is essentially necessitating a containerization strategy. At the same time, it is also likely that K8s is enabling organizations to more easily leverage multiple cloud vendors.

Our bottom line? Now is a good time to flesh out your knowledge of and experience with Kubernetes, AWS, and Azure. In our next monthly data report, we look forward to taking you into the world of skills in Data Engineering and big data analytics.