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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

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Microsoft Loves Linux and FOSS Because of Developers

Tom Petrocelli, Amalgam Insights Research Fellow
For much of the past 30 years, Microsoft was famous for its hostility toward Free and Open Source Software (FOSS). They reserved special disdain for Linux, the Unix-like operating system that first emerged in the 1990s. Linux arrived on the scene just as Microsoft was beginning to batter Unix with Windows NT. The Microsoft leadership at the time, especially Steve Ballmer, viewed Linux as an existential threat. They approached Linux with an “us versus them” mentality that was, at times, rabid.

It’s not news that times have changed and Microsoft with it. Instead of looking to destroy Linux and FOSS, Microsoft CEO Satya Nadella has embraced it.

Microsoft has begun to meld with the FOSS community, creating Linux-Windows combinations that were unthinkable in the Ballmer era.

In just the past few years Microsoft has:

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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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Lynne Baer: Clarifying Data Science Platforms for Business

Word cloud of data science software and terms

My name is Lynne Baer, and I’ll be covering the world of data science software for Amalgam Insights. I’ll investigate data science platforms and apps to solve the puzzle of getting the right tools to the right people and organizations.

“Data science” is on the tip of every executive’s tongue right now. The idea that new business initiatives (and improvements to existing ones) can be found in the data a company is already collecting is compelling. Perhaps your organization has already dipped its toes in the data discovery and analysis waters – your employees may be managing your company’s data in Informatica, or performing statistical analysis in Statistica, or experimenting with Tableau to transform data into visualizations.

But what is a Data Science Platform? Right now, if you’re looking to buy software for your company to do data science-related tasks, it’s difficult to know which applications will actually suit your needs. Do you already have a data workflow you’d like to build on, or are you looking to the structure of an end-to-end platform to set your data science initiative up for success? How do you coordinate a team of data scientists to take better advantages of existing resources they’ve already created? Do you have coders in-house already who can work with a platform designed for people writing in Python, R, Scala, Julia? Are there more user-friendly tools out there your company can use if you don’t? What do you do if some of your data requires tighter security protocols around it? Or if some of your data models themselves are proprietary and/or confidential?

All of these questions are part and parcel of the big one: How can companies tell what makes a good data science platform for their needs before investing time and money? Are traditional enterprise software vendors like IBM, Microsoft, SAP, SAS dependable in this space? What about companies like Alteryx, H2O.ai, KNIME, RapidMiner? Other popular platforms under consideration should also include Anaconda, Angoss (recently acquired by Datawatch), Domino, Databricks, Dataiku, MapR, Mathworks, Teradata, TIBCO. And then there’s new startups like Sentenai, focused on streaming sensor data, and slightly more established companies like Cloudera looking to expand from their existing offerings.

Over the next several months, I’ll be digging deeply to answer these questions, speaking with vendors, users, and investors in the data science market. I would love to speak with you, and I look forward to continuing this discussion. And if you’ll be at Alteryx Inspire in June, I’ll see you there.

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Cloudera Analyst Conference Makes The Case for Analytic & AI Insights at Scale

On April 9th and 10th, Amalgam Insights attended the fifth Cloudera’s Industry Analyst and Influencer Conference (which I’ll self-servingly refer to as the Analyst Conference since I attended as an industry analyst) in Santa Monica. Cloudera sought to make the case that it was evolving beyond the market offerings that it is currently best known for as a Hadoop distribution and commercial data lake in becoming a machine learning and analytics platform. In doing so, Cloudera was extremely self-aware of its need to progress beyond the role of multi-petabyte storage at scale to a machine learning solution.
Cloudera’s Challenges in Enterprise Machine Learning 
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Blockchain! What is it Good For?

Diamond - Immutable and Hardened
Tom Petrocelli, Amalgam Insights Contributing Analyst

Blockchain looks to be one of those up and coming technologies that is constantly being talked about. Many of the largest IT companies – IBM, Microsoft, and Oracle to name few – plus a not-for-profit or two are heavily promoting blockchain. Clearly, there is intense interest, much of it fueled by exotic-sounding cryptocurrencies such as Bitcoin and Ethereum. The big question I get asked – and analysts are supposed to be able to answer the big questions – is “What can I use blockchain for?”

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Data and Analytic Strategies for Developing Ethical IT: a BrightTALK webinar

BI to AI on Trusted Data - An Amalgam Insights Research Theme
BI to AI on Trusted Data – An Amalgam Insights Research Theme

Recommended Audience: CIOs, Enterprise Architects, Data Managers, Analytics Managers, Data Scientists, IT Managers

Vendors Mentioned: Trifacta, Paxata, Datameer, Datawatch, Lavastorm, Alation, Tamr, Unifi, 1010Data, Podium Data, IBM, Domo, Microsoft, Information Builders, Board, Microstrategy, Cloudera, H20.ai, RapidMiner, Domino Data Lab, Dataiku, TIBCO, SAS, Amazon Web Services, Google, DataRobot.

In case you missed it, I just finished up my webinar on Data and Analytic Strategies for Developing Ethical IT. We are headed into a new algorithmic, statistical, and heterogenous data-defined model of IT where IT ethics and relevance are being challenged. In this webinar, we discussed:

  • Why IT is broken from a support and business perspective
  • The aspects of IT that can be fixed
  • What we can do as IT managers to fix IT
  • Data Prep, Data Unification, Business Intelligence, Data Science, and Machine Learning vendors that can help unlock the Black Boxes and Opt-Out disasters in IT
  • Key Recommendations

This webinar provides context to my ongoing research tracks of “BI to AI on Shared Data” and “IT Management at Scale.” To attend the webinar, please check the embedded view below or click to watch on BrightTALK


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As API Management Problem Grows, Informatica Jumps into the Market

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Tom Petrocelli, Amalgam Insights Contributing Analyst

API management is a necessary but boring practice. As developers make use of a mix of public cloud, purchased or open source libraries, and homegrown services, the number of APIs used by developers quickly renders pouring through documentation impractical.

Microservices, usually accessed via RESTFul APIs, cause API calls to rapidly proliferate. Even modest-sized microservices-based systems experience API overload quickly. Agile development can exacerbate the problem of understanding and using APIs. The rapid pace of Agile, especially Scrum, leaves little time for proper documentation of APIs. Documentation often takes a back seat to continuous deployment.

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Microsoft: The New Player in Quantum Computing

Doppelspalt

On the week of September 25th, 2017, Microsoft made a huge announcement at its annual Ignite and Envision conference. Microsoft has become one of a small number of companies that is demonstrating quantum computing. IBM is another company that is also pursuing this rather futuristic computing model. For those who are not up-to-date on quantum…

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Microsoft Infuses Products with Machine Learning and the Social Graph

This past week (September 25 – 27, 2017) Microsoft held its Ignite and Envision Conferences. The co-conferences encompass both technology (Ignite) and the business of technology (Envision). Microsoft’s announcements reflected that duality with esoteric technology subjects such as mixed reality and quantum computing on equal footing with digital transformation, a mainstay of modern business transformation projects. There were two announcements that, in my opinion, will have the most impact in the short-term because they were more foundational.

The first announcement was that machine learning was being integrated into every Microsoft productivity and business product. Most large software companies are adding machine learning to their platforms but no company has Microsoft’s reach into modern businesses. Like IBM, SAP and Oracle, Microsoft can embed machine learning in business applications such as CRM. Microsoft can also integrate machine learning into productivity applications as can Google. IBM can do both but IBM’s office applications aren’t close to having the market penetration of Microsoft Office 365. Microsoft has the opportunity to embed machine learning everywhere in a business, a capability that none of their competitors have.

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