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 with no sleep for three days straight. That energy came from two sources – excitement and fear.

Two announcements, in particular, generated joy amongst the devoted Red Hat fans. The first was the announcement of Red Hat Enterprise Linux version 8, better known as RHEL8. RHEL is the granddaddy of all major Linux distributions for the data center. RHEL8, however, doesn’t seem all that old. As well as all the typical enhancements to the kernel and other parts of the distro, Red Hat has added two killer features to RHEL.

The first, the web console, is a real winner. It provides a secure browser-based system to manage all the features of Linux that one typically needs a command line on the server to perform. Now, using Telnet or SSH to log in to a remote box and do a few adjustments is no big deal when you have a small number of machines, physical or virtual, in a data center. When there are thousands of machines to care for, this is too cumbersome. With web console plus Red Hat Satellite, the same type of system maintenance is much more efficient. It even has a terminal built in if the command line is the only option. I predict that the web console will be an especially useful asset to new sysadmins who have yet to learn the intricacies of the Linux command line (or just don’t want to).

The new image builder is also going to be a big help for DevOps teams. Image builder uses a point and click interface to build images of software stacks, based on RHEL of course, that can be instantiated over and over. Creating consistent environments for developers and testing is a major pain for DevOps teams. The ability to quickly and easily create and deploy images will take away a major impediment to smooth DevOps pipelines.

The second announcement that gained a lot of attention was the impending GA of OpenShift 4 represents a major change in the Red Hat container platform. It incorporates all the container automation goodness that Red Hat acquired from CoreOS, especially the operator framework. Operators are key to automating container clusters, something that is desperately needed for large scale production clusters. While Kubernetes has added a lot of features to help with some automation tasks, such as autoscaling, that’s not nearly enough for managing clusters at hyperscale or across hybrid clouds. Operators are a step in that direction, especially as Red Hat makes it easier to use Operators.

Speaking of OpenShift, Satya Nadella, CEO of Microsoft appeared on the mainstage to help announce Azure Red Hat OpenShift. This would have been considered a mortal sin at pre-Nadella Microsoft and highlights the acceptance of Linux and open source at the Windows farm. Azure Red Hat OpenShift is an implementation of OpenShift as a native Azure service. This matters a lot to those serious about multi-cloud deployments. Software that is not a native service for a cloud service provider do not have the integrations for billing, management, and especially set up that native services do. That makes them second class citizens in the cloud ecosystem. Azure Red Hat OpenShift elevates the platform to first-class status in the Azure environment.

Now for the fear. Although Red Hat went to considerable lengths to address the “blue elephant in the room”, to the point of bringing Ginny Rometty, IBM CEO on stage, the unease around the acquisition by IBM was palpable amongst Red Hat customers. Many that I spoke to were clearly afraid that IBM would ruin Red Hat. Rometty, of course, insisted that was not the case, going so far as to say that she “didn’t spend $34B on Red Hat to destroy them.”

That was cold comfort to Red Hat partners and customers who have seen tech mergers start with the best intentions and end in disaster. Many attendees I spoke drew parallels with the Oracle acquisition of Sun. Sun was, in fact, the Red Hat of its time – innovative, nimble, and with fierce loyalists amongst the technical staff. While products created by Sun still exist today, especially Java and MySQL, the essence of Sun was ruined in the acquisition. That is a giant cloud hanging over the IBM-Red Hat deal. For all the advantages that this deal brings to both companies and the open source community, the potential for a train wreck exists and that is a source of angst in the Red Hat and open source world.

In 2019, Red Hat is looking good and may have a great future. Or it is on the brink of disaster. The path they will take now depends on IBM. If IBM leaves them alone, it may turn out to be an amazing deal and the capstone of Rometty and Jim Whitehurst’s careers. If IBM allows internal bureaucracy and politics to change the current plan for Red Hat, it will be Sun version 2. Otherwise, it is expected that Red Hat will continue to make open source enterprise-friendly and drive open source communities. That would be very nice indeed.

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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Workday Surprises the IPO Market and Acquires Adaptive Insights

Key Stakeholders: Chief Information Officers, Chief Financial Officers, Chief Operating Officers, Chief Digital Officers, Chief Technology Officer, Accounting Directors and Managers, Sales Operations Directors and Managers, Controllers, Finance Directors and Managers, Corporate Planning Directors and Managers Why It Matters: Workday snatched Adaptive Insights away from the public markets only days before IPO, acquiring a proven enterprise planning…

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