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Oracle Labs has recently (April 17, 2018) announced the 1.0 release of GraalVM. GraalVM is an open source language virtual machine(VM), much like the Java VM or Node.js virtual machine. What makes GraalVM interesting, is that it can execute code written in a variety of languages including Java (and Java VM based languages such as Scala, Groovy, or Kotlin), R, JavaScript, along with Ruby, R, and Python. This is a departure from mainstream VM designs. It is much more common to have separate and specific VMs for languages such as PHP or Python. In some cases, a language will byte compile to a different virtual machine, for instance Clojure compiling to the run on the Java VM. Those languages are purpose built to run on a specific VM. GraalVM, on the other hand, runs code written in languages originally built for their own VM.

This approach offers advantages over the traditional approach of one language, one VM. For example, any program that is compiled for GraalVM can share libraries with other programs that is likewise compiled. Developers can write in different languages but still maintain interoperability and code reuse across them all. This also allows developers to continue to use code written in “older languages” while migrating to a new one. Similarly, it allows the continued used of majority language, such as Java, while leveraging languages that are built for specific purposes, such as R. Another advantage of GraalVM is ubiquity. One VM for multiple needs means fewer VMs to provision and update across IT servers and containers. That can be a serious time saver and makes maintaining large and complex systems a bit easier.
Continue reading GraalVM is a Multi-Language Compiler Technology to Watch

I have a new paper out called “Providing a Rapid Response to Meltdown and Spectre for Hybrid IT.” It’s sponsored by CloudPassage, and the paper is free from them.

This paper is designed to help key stakeholders mitigate the risk of Meltdown and Spectre, which will be especially difficult in hybrid or mixed systems.

There are billions of PCs and mobile devices affected by Meltdown and Spectre. That’s a big problem for OS vendors. For enterprise IT, there is also the need to deal with hundreds of millions of host servers and the virtual machines running on them. Meltdown and Spectre highlight just how difficult it is to update and patch hybrid systems with hosts, virtual machines, containers, and cloud servers in the mix. Don’t despair! There are solutions.

Take action by downloading my paper, underwritten by CloudPassage: “Providing a Rapid Response to Meltdown and Spectre for Hybrid IT.”

As the year comes to a close, I have had the opportunity to reflect on what has transpired in 2017 and look ahead to 2018. Some of my recent thoughts on 2017 have been published in:

• InformationWeek: AWS Ignites Debate About the Death of IT Ops
• CMSWire: Will Microsoft Graph Deliver on the Promises of the Social Graph?
• DevOps.com: DevOps Gets More Exciting in 2018

These articles provide a peek ahead at emerging 2018 trends.

In the two areas I cover, collaboration and DevOps/Developer Trends, I plan to continue to look at:
• The continued transformation of the collaboration market. [Click to Tweet] I am expecting a “mass extinction event” of products in this space. That doesn’t mean the collaboration market will evaporate. Instead, I am looking for niche products that address specific collaboration segments to thrive while a handful of large collaboration players will consume the general market.
• The emergence of NoOps, for No Operations, in the mid-market. [Click to Tweet] The Amazon push to serverless products is a bellwether of the upcoming move toward cloud vendor operations supplanting company IT sysops.
• 2018 will be the year of the container.[Click to Tweet] Containers have been growing in popularity over the past several years but 2018 will be the year when they become truly mass market. The growth in the ecosystem, especially the widespread availability of cloud Kubernetes services, will make containers more palatable to a wider market.
• Integrated DevOps pipelines will make DevOps more efficient… if [Click to Tweet] we can get the politics out of IT.
• Machine learning will continue to be integrated into developer tools [Click to Tweet] which, in turn, will make more complex coding and deployment jobs easier.

As you know, I joined Amalgam Insights in September. Amalgam Insights, or AI, is a full-service market analyst firm. I’d welcome the opportunity to learn more about what 2018 holds for you. Perhaps we can schedule a quick call in the next couple of weeks. Let me know what works best for you. As always, if I can provide any additional information about AI, I’d be happy to do so!

Thanks, and have a happy holiday season.

For more predictions on IT management at scale, check out Todd Maddox’s 5 Predictions That Will Transform Corporate Training.

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:
Continue reading 28 Hours as an Industry Analyst at Strata Data 2017