Kubernetes Grows Up – The View from KubeCon EU 2019

Our little Kubernetes is growing up.

By “growing up” I mean it is almost in a state that a mainstream company can consider it fit for production. While there are several factors that act as a drag against mainstream reception, a lack of completeness has been a major force against Kubernetes broader acceptance. Completeness, in this context, means that all the parts of an enterprise platform are available off the shelf and won’t require a major engineering effort on the part of conventional IT departments.

The good news from KubeCon+CloudNativeCon EU 2019 in Barcelona, Spain (May 20 – 23 2019) is that the Kubernetes and related communities are zeroing in on that ever so important target. There are a number of markers pointing toward mainstream acceptance. Projects are filling out the infrastructure – gaining completeness – and the community is growing.

Project Updates

While Kubernetes may be at the core, there are many supporting projects that are striving to add capabilities to the ecosystem that will result in a more complete platform for microservices. Some of the projects featured in the project updates show the drive for completeness. For example, OpenEBS and Rook are two projects striving to make container storage more enterprise friendly. Updates to both projects were announced at the conference. Storage, like networking, is an area that must be tackled before mainstream IT can seriously consider container microservices platforms based on Kubernetes.

Managing microservices performance and failure is a big part of the ability to deploy containers at scale. For this reason, the announcement that two projects that provide application tracing capabilities, OpenTracing and OpenCensus, were merging into OpenTelemetry is especially important. Ultimately, developers need a unified approach to gathering data for managing container-based applications at scale. Removing duplication of effort and competing agendas will speed up the realization of that vision.

Also announced at KubeCon+CloudNativeCon EU 2019 were updates to Helm and Harbor, two projects that tackle thorny issues of packaging and distributing containers to Kubernetes. These are necessary parts of the process of deploying Kubernetes applications. Securely managing container lifecycles through packaging and repositories is a key component of DevOps support for new container architectures. Forward momentum in these projects is forward movement toward the mainstream.

There were other project updates, including updates to Kubernetes itself and Crio-io. Clearly, the community is filling in the blank spots in container architectures, making Kubernetes a more viable application platform for everyone.

The Community is Growing

Another gauge pointing toward mainstream acceptance is the growth in the community. The bigger the community, the more hands to do the work and the better the chances of achieving feature critical mass. This year in Barcelona, KubeCon+CloudNativeCon EU saw 7700 attendees, nearly twice last year in Copenhagen. In the core Kubernetes project, there are 164K commits and 1.2M comments in Github. This speaks to broad involvement in making Kubernetes better. Completeness requires lots of work and that is more achievable when there are more people involved.

Unfortunately, as Cheryl Hung, Director of Ecosystems at CNCF says, only 3% of contributors are women. The alarming lack of diversity in the IT industry shows up even in Kubernetes despite the high-profile women involved in the conference such as Janet Kuo of Google. Diversity brings more and different ideas to a project and it would be great to see the participation of women grow.

Service Mesh Was the Talk of the Town

The number of conversations I had about service mesh was astounding. It’s true that I had released a pair of papers on it, one just before KubeCon+CloudNativeCon EU 2019. That may have explained why people want to talk to me about it but not the general buzz. There was service mesh talk in the halls, at lunch, in sessions, and from the mainstage. It’s pretty much what everyone wanted to know about. That’s not surprising since a service mesh is going to be a vital part of large scale-out microservices applications. What was surprising was that even attendees who were new to Kubernetes were keen to know more. This was a very good omen.

It certainly helped that there was a big service mesh related announcement from the mainstage on Tuesday. Microsoft, in conjunction with a host of companies, announced the Service Mesh Interface. It’s a common API for different vendor and project service mesh components. Think of it as a lingua franca of service mesh. There were shout-outs to Linkerd and Solo.io. The latter especially had much to do with creating SMI. The fast maturation of the service mesh segment of the Kubernetes market is another stepping stone toward the completeness necessary for mainstream adoption.

Already Way Too Many Distros

There were a lot of Kubernetes distributions a KubeCon+CloudNativeCon EU 2019. A lot. Really.  A lot. While this is a testimony the growth in Kubernetes as a platform, it’s confusing to IT professionals making choices. Some are managed cloud services; others are distributions for on-premises or when you want to install your own on a cloud instance. Here’s some of the Kubernetes distros I saw on the expo floor.  I’m sure I missed a few:

Microsoft Azure Google Digital Ocean Alibaba
Canonical (Ubuntu) Oracle IBM Red Hat
VMWare SUSE Rancher Pivotal
Mirantis Platform9

 

From what I hear this is a sample, not a comprehensive, list. The dark side of this enormous choice is confusion. Choosing is hard when you get beyond a handful of options. Still, only five years into the evolution of Kubernetes, it’s a good sign to see this much commercial support for it.

The Kubernetes and Cloud Native architecture is like a teenager. It’s growing rapidly but not quite done. As the industry fills in the blanks and as communities better networking, storage, and deployment capabilities, it will go mainstream and become applicable to companies of all sizes and types. Soon. Not yet but very soon.

How Red Hat Runs

This past week at Red Hat Summit 2019 (May 7 – 9 2019) has been exhausting. It’s not an overstatement to say that they run analysts ragged at their events, but that’s not why the conference made me tired. It was the sheer energy of the show, the kind of energy that keeps you running…

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Network Big Iron f5 Acquires Software Network Vendor NGINX

I woke up last Tuesday (March 12, 2019) to find an interesting announcement in my inbox. NGINX, the software networking company, well known for its NGINX web server/load balancer, was being acquired by f5. f5 is best known for its network appliances which implement network security, load balancing, etc. in data centers. The deal was…

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Tom Petrocelli Clarifies How Cloud Foundry and Kubernetes Provide Different Paths to Microservices

DevOps Research Fellow Tom Petrocelli has just published a new report describing the roles that Cloud Foundry Application Runtime and Kubernetes play in supporting microservices. This report explores when each solution is appropriate and provides a set of vendors that provide resources and solutions to support the development of these open source projects.

Organizations and Vendors mentioned include: Cloud Foundry Foundation, Cloud Native Computing Foundation, Pivotal, IBM, Suse, Atos, Red Hat, Canonical, Rancher, Mesosphere, Heptio, Google, Amazon, Oracle, and Microsoft

To download this report, which has been made available at no cost until the end of February, go to https://amalgaminsights.com/product/analyst-insight-cloud-foundry-and-kubernetes-different-paths-to-microservices

Data Science and Machine Learning News Roundup, January 2019

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Cloudera and Hortonworks Complete Planned Merger

In early January, Cloudera and Hortonworks completed their planned merger. With this, Cloudera becomes the default machine learning ecosystem for Hadoop-based data, while providing an easy pathway for expanding into  machine learning and analytics capabilities for Hortonworks customers.

Study: 89 Percent of Finance Teams Yet to Embrace Artificial Intelligence

A study conducted by the Association of International Certified Professional Accountants (AICPA) and Oracle revealed that 89% of organizations have not deployed AI to their finance groups. Although a correlation exists between companies with revenue growth and companies that are using AI, the key takeaway is that artificial intelligence is still in the early adopter phase for most organizations.

Gartner Magic Quadrant for Data Science and Machine Learning Platforms

In late January, Gartner released its Magic Quadrant for Data Science and Machine Learning Platforms. New to the Data Science and Machine Learning MQ this year are both DataRobot and Google – two machine learning offerings with completely different audiences and scope. DataRobot offers an automated machine learning service targeted towards “citizen data scientists,” while Google’s machine learning tools, though part of Google Cloud Platform, are more of a DIY data pipeline targeted towards developers. By contrast, I find it curious that Amazon’s SageMaker machine learning platform – and its own collection of task-specific machine learning tools, despite their similarity to Google’s – failed to make the quadrant, given this quadrant’s large umbrella.

While data science and machine learning are still emerging markets, the contrasting demands of these technologies made by citizen data scientists and by cutting-edge developers warrants splitting the next Data Science and Machine Learning Magic Quadrant into separate reports targeted to the considerations of each of these audiences. In particular, the continued growth of automated machine learning technologies will likely drive such a split, as citizen data scientists pursue a “good enough” solution that provides quick results.

Oracle Delivers a FOSS Surprise

An unfortunate side effect of being an industry analyst is that it is easy to become jaded. There is a tendency to fall back into stereotypes about technology and companies. Add to this nearly 35 years in computer technology and it would surprise no one to hear an analyst say, “Been there, done that, got…

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Data Science Platforms News Roundup, August 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta.

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Oracle GraphPipe: Expediting and Standardizing Model Deployment and Querying

On August 15, 2018, Oracle announced the availability of GraphPipe, a network protocol designed to transmit machine learning data between remote processes in a standardized manner, with the goal of simplifying the machine learning model deployment process. The spec is now available on Oracle’s GitHub, along with clients and servers that have implemented the spec for Python and Go (with a Java client soon to come); and a TensorFlow plugin that allows remote models to be included inside TensorFlow graphs.

Oracle’s goal with GraphPipe is to standardize the process of model deployment regardless of the frameworks utilized in the model creation stage.

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What Data Science Platform Suits Your Organization’s Needs?

This summer, my Amalgam Insights colleague Hyoun Park and I will be teaming up to address that question. When it comes to data science platforms, there’s no such thing as “one size fits all.” We are writing this landscape because understanding the processes of scaling data science beyond individual experiments and integrating it into your business is difficult. By breaking down the key characteristics of the data science platform market, this landscape will help potential buyers choose the appropriate platform for your organizational needs. We will examine the following questions that serve as key differentiators to determine appropriate data science platform purchasing solutions to figure out which characteristics, functionalities, and policies differentiate platforms supporting introductory data science workflows from those supporting scaled-up enterprise-grade workflows.

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