Pivotal Podcasts

Turns out digital transformation is not just for traditional enterprises. Software companies need to adapt too. That's true for Dynatrace, which started life nearly 25 years ago as an application performance management vendor. As its customers began developing modern, customer-centric applications on platforms like Pivotal Cloud Foundry, Dynatrace went through its own transformation. Today, Dynatrace is a leading digital performance management provider that helps its customers connect the dots between application performance and the impact on the customer experience. In this episode of Pivotal Insights, host Jeff Kelly speaks with Mike Villiger, Senior Technical Partner Manager at Dynatrace. The two discuss the evolution of the application performance management market, Dynatrace's data-driven approach to customer experience optimization, and how together Dynatrace and Pivotal are helping their joint enterprise customers transform how they build software and delight customers with compelling software-based experiences, and more. Show Notes: https://content.pivotal.io/podcasts/optimizing-the-customer-experience-with-dynatrace-and-pivotal-cloud-foundry

What's the best way to categorize and prioritize your IT projects? Splitting them up between systems of record (ERP) and systems of engagement (user-facing apps) is a popular mode of thinking, highly related to bi-modal IT. In this episode, guest Ian Andrews explains why this framing is a bad idea and offers a value-driven way of thinking about it instead, along with plenty of commentary from Coté and Richard. See full show notes at: http://cote.io/conversations49

How do containers fit into your cloud native planning? That's a the question we start with this week, with (returning guest) John Feminella. We quickly arrive at a conversation on the larger question which is how to build a cloud platform and the allure of building it yourself. Also, we cover recent news in the infrastructure software space. Show notes: https://content.pivotal.io/podcasts/avoid-the-ninja-anti-pattern-planning-out-your-cloud-platform-project

When a CEO or other C-level exec wants answers, he or she typically wants them yesterday. Since we can’t change the properties of space or time, the next-best-solution, so many of us believe, is to develop real-time analytics tools, such as real-time dashboards, to keep executives and other VIPs up-to-date on how the business is performing. This way, executives don’t even have to ask. The data required is displayed on their laptops, whizzing by at real-time speed. But are such real-time analytics tools really the best way to provide executives and others with actionable intelligence? How much information can someone absorb by watching a stream of high-velocity data? Executives say they want real-time analytics, but they may be better served by, for example, event-oriented insights that surface only when action is required. In fact, there are a number of considerations to real-time analytics - including just defining what the business means by “real-time.” In this episode of Pivotal Insights, Jeff Kelly and Dormain Drewitz grapple with the thorny issue of real-time analytics and how to make the right business and architectural decisions to support your organization's analytics needs.

One of your favorite technologies is on the death wagon, again. Gartner recently recommended avoiding JEE for new, cloud native application development. This predictably kicked up all sorts of push-back from the JEE stalwarts. In this episode we discuss the report, the responses, and all the context to figure out what to make of all this. Spoiler: JEE isn't dead, as ever, it's just a part of the ongoing gumbo that is a Java application. See full show notes at http://cote.io/conversations47

Poor Juan Ponce de Leon. The Spanish explorer spent years searching for the Fountain of Youth. Instead, all he discovered was Florida. Many in the database business have likewise spent years chasing a myth: a single database that can simultaneously support both high-performance, high-speed transactional processing and large-scale advanced analytics and data science. In both cases, the object of desire never existed in the first place, And In fact, the story of Juan’s search for the mythical fountain is itself a myth. But the search for a magical database is all too real. The truth is, trade-offs are required for any single database to support both transactions and analytics. Either transaction performance takes a hit or the amount of data available for analysis must be limited. For some use cases, these trade-offs are acceptable. But for use cases when both high-performance transactions and Big Data analytics are required, the better approach is to seamlessly connect two best-of-breed solutions, such as Pivotal GemFire, a Java-based transactional in-memory data grid, and Pivotal Greenplum, a massively parallel processing analytical database. In this episode of Pivotal Insights, host Jeff Kelly speaks with Pivotal’s Ivan Novick and Jag Mirani about the new GemFire-Greenplum Connector that enables users to connect the two solutions to support intelligent applications at scale.

We don't have enough people, and the people we have don't have the right skills. That's a gasp oft heard during the machinations of digital transformation. To investigate this sentiment, the Cloud Foundry Foundation recently fielded a survey to probe into both sentiment around developer skills and how organizations are addressing it. The findings were actually optimistic, but there's still work to be done. In this episode, we dig into this survey and what the findings mean for how IT departments need rethink their approach to training and hiring. To do so, we invited Abby Kearns and James Governor. Abby is the Executive Director of the Cloud Foundry Foundation who did the survey. James is one of the founders of the analyst firm RedMonk. See full show notes: http://pivotal.io/podcast

Depending on what you read, machine learning (ML) and artificial intelligence (AI) are either going to revolutionize the world we live in or cause its destruction. Some think ML and AI will lead to cures for cancer, for example, while others worry ML and AI will pave the way for intelligent killer robots that annihilate mankind. As with most things in life, the truth lies somewhere in between. In this episode of Pivotal Insights, host Jeff Kelly speaks with Frank McQuillan, Director of Product Management at Pivotal for Apache MADlib, an open source-based machine learning library. The two talk about the realities of ML and AI in the enterprise, discuss the potential impact of increasing automation on jobs, share examples of Pivotal customers that are solving real business problems with ML today, and offer tips to enterprise practitioners for identifying potential valuable ML and AI use cases.

How are analysts reckoning with "cloud native"? Rita Manachi joins us again to talk about industry analysts and what they're up to. We briefly recap what analysts relations (AR) does, and then jump into how analysts are thinking about Pivotal now. There's several new reports out that are good reads for the Pivotal-minded. Having just talked with several analysts over some chafer warmed lunch, we discuss how analyst meetings go and what to get out of them. We also cover recent news, primarily, the slew of announcements out of AWS re:Invent last week. Full show notes: http://pivotal.io/podcast

In this episode of Pivotal Insights, host Jeff Kelly and Pivotal Data Scientist Ian Huston talk about the potential benefits of bringing data scientists into the balanced team, best practices for doing so, and put the myth of the Data Science Unicorn to rest.

Home Depot has been using Pivotal Cloud Foundry and developing in the Pivotal way for over a year now. Thus far, they have roughly 150 applications running in Pivotal Cloud Foundry across all parts of their business. While at Gartner's Application Strategies & Solutions Summit, we talk with Tony McCulley about Home Depot's journey putting cloud native thinking and technologies in place. Tony had just given a talk about this experience so we all had the topics fresh in out minds. There are two great talks Tony's given before on this topic: one from 2015 at a MeetUp, and another from SpringOne Platform. Tony's great for talking about what works, what doesn't work, and how to plan out transforming from the "old way" to the "new way" of doing IT. See full show notes: https://content.pivotal.io/podcasts/045-cloud-native-at-home-depot-with-tony-mcculley

If you plan to simply reassign your SQL Server DBAs to a massively parallel processing (MPP) analytical database, you might want to think again. There are subtle but important differences between managing and administering a symmetric multi-processing, or SMP, database like SQL Server and an MPP analytical database like Pivotal Greenplum. While many enterprises choose to train their DBAs to master MPP database administration, others prefer to partner with an expert for the task. That’s why Pivotal is introducing Pivotal Data Operations Services, or Pivotal DOS. Pivotal DOS is a managed service offering for Pivotal Greenplum and Pivotal HDB, the leading Hadoop native SQL database for analytics. In this episode of Pivotal Insights, host Jeff Kelly speaks with Jacque Istok, head of field engineering for Pivotal data, about the new service and the reasons behind its creation. Among them? Pivotal DOS allows enterprises to leave the heavy lifting of administering and managing the database to Pivotal, and instead focus on analyzing data and operationalizing insights. Listen for more details on the service and best practices for getting started with MPP analytics.

We're seeing more adoption of agile in large organizations than ever before. More interestingly, they're really doing it, totally transforming the multiple layers of process to boil down to the the leanest bucket of parts that ensure quality, useful software. While there's a lot - a lot! - of work to be done, there's a slew of useful best practices, stories, and anecdotes emerging. In this episode we discuss this general trend and two of the related topics: scaling agile up and pair programming. We also cover spate of recent news about Cloud Foundry performance, .Net rolling out into various ecosystems, the new leadership at the Cloud Foundry Foundation, and lobster eggs benedict.

Welcome to the Pivotal Life podcast! A place for us to share our culture, experiences and agile practices here at Pivotal Labs. For the very first episode, Parker and Caroline from the NY office, will be sharing with you how we begin the first 40 minutes of our days at Labs.

PowerPoint is a great presentation tool, but it is also the final resting place for many data science initiatives. “PowerPoint,” says Kaushik Das, “is where models go to die.” If you’re a data scientist, you know what he’s talking about. Das, who heads the data science practice at Pivotal, argues operationalizing predictive models in applications and business logic is the keys to saving data science models from this grim fate. In this episode of Pivotal Insights, host Jeff Kelly and Das talk about why operationalizing data science models is so important and why so many enterprises struggle to do so. Turns out, technology is only part of the issue. Das provides tips on how to reframe the approach to data science in order to industrialize the process of getting insights to the right people at the right time on an ongoing basis.