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 described as creating a way to “bridge NetOps to DevOps.” That’s a good way to characterize the value of this acquisition. Networking has begun to evolve, or perhaps devolve, from the data center into the container cluster. Network services that used to be the domain of centralized network devices, especially appliances, may be found in small footprint software that runs in containers, often in a Kubernetes pod. It’s not that centralized network resources don’t have a place – you wouldn’t be able to manage the infrastructure that container clusters run on without them. Instead, both network appliances and containerized network resources, such as a service mesh, will be present in microservices architectures. By combining both types of network capabilities, f5 will be able to sell a spectrum of network appliances and software tailored toward different types of architectures. This includes the emerging microservices architectures that are quickly becoming mainstream. With NGINX, f5 will be well positioned to meet the network needs of today and of the future.

The one odd thing about this acquisition is that f5 already has an in-house project, Aspen Mesh, to commercialize very similar software. Aspen Mesh sells an Istio/Envoy distribution that extends the base features of the open source software. There is considerable overlap between Aspen Mesh and NGINX, at least in terms of capabilities. Both provide software to enable a service mesh and provide services to virtual networks. ” Sure, NGINX has market share (and brain share) but $670M is a lot of money when you already have something in hand.

NGINX and f5 say that they see the products as complementary and will allow f5 to build a continuum of offerings for different needs and scale. In this regard, I would agree with them. Aspen Mesh and NGINX are addressing the same problems but in different ways. By combining NGINX with the Aspen Mesh, f5 can cover more of the market.

Given the vendor support of Istio/Envoy in the market, it’s hard to imagine f5 just dropping Aspen Mesh. At present, f5 plans to operate NGINX separately but that doesn’t mean they won’t combine NGINX with Aspen Mesh in the future. Some form of coexistence is necessary for f5 to leverage all the investments in both brands.

The open source governance question may be a problem. There is nervousness within the NGINX community about its future. NGINX is based on its own open source project, one not controlled by any other vendors. The worry is that the NGINX community run into the same issues that the Java and MySQL communities did after they were acquired by Oracle which included changes to licensing and issues over what constituted the open source software versus the enterprise, hence proprietary software. f5 will have to reassure the NGINX community or risk a fork of the project or, worse, the community jumping ship to other projects. For Oracle, that led to MariaDB and a new rival to MySQL.

NGINX will give f5 both opportunity and technology to address emerging architectures that their current product lines will not. Aspen Mesh will still need time to grow before it can grab the brain share and revenue that NGINX already has. For a mainstream networking company like f5, this acquisition gets them into the game more quickly, generates revenue immediately, and does so in a manner that is closer to their norm. This makes a lot of sense.

Now that the first acquisition has happened, the big question will be “who are the next sellers and the next buyers?” I would predict that we will see more deals like this one. We will have to wait and see.

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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Data Science Platforms News Roundup, June 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:

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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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Market Milestone: Oracle Builds Data Science Gravity By Purchasing DataScience.com

Bridging the Gap

Industry: Data Science Platforms

Key Stakeholders: IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, current DataScience.com customers, current Oracle customers

Why It Matters: Oracle released a number of AI tools in Q4 2017, but until now, it lacked a data science platform to support complete data science workflows. With this acquisition, Oracle now has an end-to-end platform to manage these workflows and support collaboration among teams of data scientists and business users, and it joins other major enterprise software companies in being able to operationalize data science.

Top Takeaways: Oracle acquired DataScience.com to retain customers with data science needs in-house rather than risk losing their data science-based business to competitors. However, Oracle has not yet not defined a timeline for rolling out the unified data science platform, or its future availability on the Oracle Cloud.

Oracle Acquires DataScience.com

On May 16, 2018, Oracle announced that it had agreed to acquire DataScience.com, an enterprise data science platform that Oracle expects to add to the Oracle Cloud environment. With Oracle’s debut of a number of AI tools last fall, this latest acquisition telegraphs Oracle’s intent to expedite its entrance into the data science platform market by buying its way in.

Oracle is reviewing DataScience.com’s existing product roadmap and will supply guidance in the future, but they mean to provide a single unified data science platform in concert with Oracle Cloud Infrastructure and its existing SaaS and PaaS offerings, empowering customers with a broader suite of machine learning tools and a complete workflow.

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