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Looking at Microservices, Containers, and Kubernetes with 2020 Vision

Some years are easy to predict than others. Stability in a market makes tracing the trend line much easier. 2020 looks to be that kind of year for the migration to microservices: stable with steady progression toward mainstream acceptance.

There is little doubt that IT organizations are moving toward microservices architectures. Microservices, which deconstruct applications into many small parts, removes much of the friction that is common in n-Tier applications when it comes to development velocity. The added resiliency and scalability of microservices in a distributed system are also highly desirable. These attributes promote better business agility, allowing IT to respond to business needs more quickly and with less disruption while helping to ensure that customers have the best experience possible.

Little in this upcoming year seems disruptive or radical; That big changes have already occurred. Instead, this is a year for building out and consolidating; Moving past the “what” and “why” and into the “how” and “do”.

Kubernetes will be top of mind to IT in the coming year. From its roots as a humble container orchestrator – one of many in the market – Kubernetes has evolved into a platform for deploying microservices into container clusters. There is more work to do with Kubernetes, especially to help autoscale clusters, but it is now a solid base on which to build modern applications.

No one should delude themselves into thinking that microservices, containers, and Kubernetes are mainstream yet. The vast majority of applications are still based on n-Tier design deployed to VMs. That’s fine for a lot of applications but businesses know that it’s not enough going forward. We’ve already seen more traditional companies begin to adopt microservices for at least some portion of their applications. This trend will accelerate in the upcoming year. At some point, microservices and containers will become the default architecture for enterprise applications. That’s a few years from now but we’ve already on the path.

From a vendor perspective, all the biggest companies are now in the Kubernetes market with at least a plain vanilla Kubernetes offering. This includes HPE and Cisco in addition to the companies that have been selling Kubernetes all along, especially IBM/Red Hat, Canonical, Google, AWS, VMWare/Pivotal, and Microsoft. The trick for these companies will be to add enough unique value that their offerings don’t appear generic. Leveraging traditional strengths, such as storage for HPE, networking for Cisco, and Java for Red Hat and VMWare/Pivotal, are the key to standing out in the market.

The entry of the giants in the Kubernetes space will pose challenges to the smaller vendors such as Mirantis and Rancher. With more than 30 Kubernetes vendors in the market, consolidation and loss is inevitable. There’s plenty of value in the smaller firms but it will be too easy for them to get trampled underfoot.

Expect M&A activity in the Kubernetes space as bigger companies acquihire or round out their portfolios. Kubernetes is now a big vendor market and the market dynamics favor them.

If there is a big danger sign on the horizon, it’s those traditional n-Tier applications that are still in production. At some point, IT will get around to thinking beyond the shiny new greenfield applications and want to migrate the older ones. Since these apps are based on radically different architectures, that won’t be easy. There just aren’t the tools to do this migration well. In short, it’s going to be a lot of work. It’s a hard sell to say that the only choices are either expensive migration projects (on top of all that digital transformation money that’s already been spent) or continuing to support and update applications that no longer meet business needs. Replatforming, or deploying the old parts to the new container platform, will provide less ROI and less value overall. The industry will need another solution.

This may be an opportunity to use all that fancy AI technology that vendors have been investing in to create software to break down an old app into a container cluster. In any event, the migration issue will be a drag on the market in 2020 as IT waits for solutions to a nearly intractable problem.

2020 is the year of the microservice architecture.

Even if that seems too dramatic, it’s not unreasonable to expect that there will be significant growth and acceleration in the deployment of Kubernetes-based microservices applications. The market has already begun the process of maturation as it adapts to the needs of larger, mainstream, corporations with more stringent requirements. The smart move is to follow that trend line.

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From #KubeCon, Three Things Happening with the Kubernetes Market

This year’s KubeCon+CloudNativeCon was, to say the least, an experience. Normally sunny San Diego treated conference-goers to torrential downpours. The unusual weather turned the block party event into a bit of a sog. My shoes are still drying out. The record crowds – this year’s attendance was 12,000 up from last year’s 8000 in Seattle – made navigating the show floor a challenge for many attendees.

Despite the weather and the crowds, this was an exciting KubeCon+CloudNativeCon. On display was the maturation of the Kubernetes and container market. Both the technology and the best practices discussions were less about “what is Kubernetes” and, instead more about “how does this fit into my architecture?” and “how enterprise-ready is this stuff?” This shift from the “what” to the “how” is a sign that Kubernetes is heading quickly to the mainstream. There are other indicators at Kubecon+CloudNativeCon that, to me, show Kubernetes maturing into a real enterprise technology.

First, the makeup of the Kubernetes community is clearly changing. Two years ago, almost every company at KubeCon+CloudNativeCon was some form of digital forward company like Lyft or cloud technology vendor such as Google or Red Hat. Now, there are many more traditional companies on both the IT and vendor side. Vendors such as HPE, Oracle, Intel, and Microsoft, mainstays of technology for the past 30 years, are here in force. Industries like telecommunications (drawn by the promise of edge computing), finance, manufacturing, and retail are much more visible than they were just a short time ago. While microservices and Kubernetes are not yet as widely deployed as more traditional n-Tier architectures and classic middleware, the mainstream is clearly interested.

Another indicator of the changes in the Kubernetes space is the prominence of security in the community. Not only are there more vendors than ever, but we are seeing more keynote time given to security practices. Security is, of course, a major component of making Kubernetes enterprise-ready. Without solid security practices and technology, Kubernetes will never be acceptable to a broad swatch of large to mid-sized businesses. That said, there is still so much more that needs to be done with Kubernetes security. The good news is that the community is working on it.

Finally, there is clearly more attention being paid to operating Kubernetes in a production environment. That’s most evident in the proliferation of tracing and logging technology, from both new and older companies, that were on display on the show floor and mainstage. Policy management was also an important area of discussion at the conference. These are all examples of the type of infrastructure that Operations teams will need to manage Kubernetes at scale and a sign that the community is thinking seriously about what happens after deployment.

It certainly helps that a lot of basic issues with Kubernetes have been solved but there is still more work to do. There are difficult challenges that need attention. How to migrate existing stateful apps originally written in Java and based on n-Tier architectures is still mostly an open question. Storage is another area that needs more innovation, though there’s serious work underway in that space. Despite the need for continued work, the progress seen at KubeCon+CloudNativeCon NA 2019 point to future where Kubernetes is a major platform for enterprise applications.  2020 will be another pivotal year for Kubernetes, containers, and microservices architectures. It may even be the year of mainstream adoption. We’ll be watching.

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28 Hours as an Industry Analyst at Strata Data 2017

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Companies Mentioned: Aberdeen Group, Actian, Alation, Arcadia Data, Attunity, BMC, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataKitchen, Datameer, Datarobot, Domino Data Lab, EMA, HPE, Hurwitz and Associates, IBM, Informatica, Kogentix, LogTrust, Looker, < MesoSphere, Micro Focus, Microstrategy, Ovum, Paxata, Podium Data, Qubole, SAP, Snowflake, Strata Data, Tableau, Tamr, Tellius, Trifacta.

Last week, I attended Strata Data Conference at the Javitz Center in New York City to catch up with a wide variety of data science and machine learning users, enablers, and thought leaders. In the process, I had the opportunity to listen to some fantastic keynotes and to chat with 30+ companies looking for solutions, 30+ vendors presenting at the show, and attend with a number of luminary industry analysts and thought leaders including Ovum’s Tony Baer, EMA’s John Myers, Aberdeen Group’s Mike Lock, and Hurwitz & Associates’ Judith Hurwitz.

From this whirwind tour of executives, I took a lot of takeaways from the keynotes and vendors that I can share and from end users that I unfortunately have to keep confidential. To give you an idea of what an industry analyst notes, following are a short summary of takeaways I took from the keynotes and from each vendor that I spoke to:

Keynotes: The key themes that really got my attention is the idea that AI requires ethics, brought up by Joanna Bryson, and that all data is biased, which danah boyd discussed. This idea that data and machine learning have their own weaknesses that require human intervention, training, and guidance is incredibly important. Over the past decade, technologists have put their trust in Big Data and the idea that data will provide answers, only to find that a naive and “unbiased” analysis of data has its own biases. Context and human perspective are inherent to translating data into value: this does not change just because our analytic and data training tools are increasingly nuanced and intelligent in nature.

Behind the hype of data science, Big Data, analytic modeling, robotic process automation, DevOps, DataOps, and artifical intelligence is this fundamental need to understand that data, algorithms, and technology all have inherent biases as the following tweet shows:

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