Pivotal Podcasts

Companies that want to get better at software are staffing and organizing themselves in new ways. The traditional "silos" approach clusters teams together into functional groups, whereas modern approaches cluster around product. We cover skills by looking at a recent Cloud Foundry Foundation survey on developer skills and then discuss some sections of Coté's upcoming cloud native journey booklet related to team composition and outsourcing. While news is sparse this week, we point to some "what does the US election mean for tech" news and also cover Microsoft Teams in relation to how "chat ops" has been extended to be SOP in most modern IT shops or, rather, how it should be.

The data warehouse market has experienced some interesting developments over the last few months and years. First, Actian decided to leave the market all together over the summer, pulling its analytical database and SQL-on-Hadoop offering from the market. Not long after that, HPE announced it was divesting its software business, which includes Vertica, to a British conglomerate with virtually no U.S. market presence. Meanwhile, data warehouse appliance stalwarts IBM Netezza and Teradata have been struggling for the last couple of years to adjust to changes in customer requirements. On the flip side, Pivotal Greenplum, the only open source-based MPP analytical database on the market, continues to gain momentum with enterprises looking for a modern data warehouse to support data science and advanced analytics workloads. So where does this leave us? In this episode of Pivotal Insights, host Jeff Kelly speaks to a group of Pivotal data engineering leaders and Pivotal’s head of data sales about what they are hearing from customers and prospects that are trying to make sense of their data warehouse options. They offer unvarnished feedback from the field, including the customer reaction to the struggles of vendors like Actian, Vertica and Teradata, thoughts on why so many are turning to Pivotal Greenplum, as well as the role open source, cloud and MPP architecture play as enterprise customers evaluate their data warehouse options (hint: they’re really important.)

Containers are as big a deal in the Cloud Foundry world as anywhere else; what was once an obscure method of process isolation is a good way to boost developer productivity. In this episode we talk with Pivotal's Onsi Fakhouri and James Bayer about containers and Pivotal Cloud Foundry. After discussing the history of containers, we talk about how containers are supported in Pivotal Cloud Foundry, and then discuss how to think through the use of containers versus buildpacks, or using containers at all. See full show notes here: https://blog.pivotal.io/pivotal-conversations

It's a question just about every enterprise asks itself: How can we monetize our data? On the surface, the question makes sense. It's cheaper than ever today to store data thanks to Hadoop and cloud storage, so many enterprises are sitting on huge volumes of customer, product, market and other data. It's only logical to think about turning all that data into new revenue, right? Not quite, says Dormain Drewitz. Starting with your data and trying to find the money is, Drewitz argues, labor-intensive, inefficient and - most importantly - rarely successful. In this episode of Pivotal Insights, Drewitz, head of product marketing for Pivotal data, and host Jeff Kelly chat about these contrasting approaches to data and analytics, and share some best practices for getting the most value from data.

There's no end of discussion about the Internet of Things now-a-days, but much of it is either about flashing toothbrushes or crazy-making huge numbers with abstract use cases. This week we talk with Pivotal's Saurabh Gupta about the work he's been doing in the IoT space with Pivotal customers. He has a great model illustrating how to think about IoT use cases which we cover in-depth, with several examples. At the end of our discussion, you'll have a good appreciation of IoT is improving the business of "the big, noisy, dirty machines." We also discuss some recent news: clouderati's new jobs, CenturyLink buying Level 3, new MacBooks and Surfaces, AI market-sizing hijinks, and an example of cloud native business thinking in the hotel industry. See full show notes: https://blog.pivotal.io/pivotal-conversations

Software development has gone through a radical shift over the last 15 years. First, agile methodologies completely upended traditional software development practices, allowing developers to write and release code much more frequently. But what good is that if you can't operationalize all that great software? Not much. So around 2008, Pivotal's own Andrew Clay Shafer spearheaded the DevOps movement, a new approach to the software development lifecycle that makes developing software and operating it a shared responsibility. The result is continuous software development and deployment in a virtuous feedback loop. Unfortunately, the world of data analytics wasn't included in the the revolution. In many enterprises today, it takes weeks, even months to get analytics and data warehouse projects off the ground and into production. This raises the question: Does data need its own DevOps moment? In this episode of Pivotal Insights, host Jeff Kelly speaks with Elisabeth Hendrickson, head of R&D for Pivotal's data portfolio, about how agile and DevOps might be applied to data analytics, what it would mean from a people and process perspective, and how data analytics technologies would need to evolve to support it.

Distributed systems are hard. Building a microservices architecture that supports evolutionary changes without breaking “contracts” among services? Especially hard. In this podcast, we grabbed Oliver Gierke, Kenny Bastani, and Andrew Clay Shafer to talk about inter-service communication, consumer-driven contract testing, and service versioning. Listen in as we wrestle with tricky concepts, and still end up as friends. See full show notes at https://blog.pivotal.io/pivotal-conversations.

Live to Tape from DellEMCWorld (Ep. 38) by Pivotal Software

Unlike the wall between application developers and application operators, which has come tumbling down at many enterprises thanks to emergence of DevOps, the wall between developers and data professionals remains standing. RedMonk analyst Stephen O’Grady covers the issue in a recent blog post, noting, “For all that the process of developing software has evolved … the database remains curiously overlooked.. In this episode of Pivotal Insights, host Jeff Kelly and Pivotal’s Jacque Istok pick up the conversation. Istok, who runs Pivotal’s data engineering and data science field organization, says the wall is starting to crack, as more enterprises recognize the role data and analytics play in developing compelling enterprise and consumer applications. But enterprises need to overcome years of ingrained behavior and mindsets in which many developers considered DBAs and other data pros more as roadblocks to success than partners.

When you're moving fast, things will break more often. It's little wonder, then, that with a microservices approach you need to pay close attention to ensuring the safe, yet speedy change to APIs. The idea of "consumer-driven contracts" has been percolating for a long time. The idea is to shift the "power" in the relationship between the provider of APIs and the consumer of those APIs more to the consumers. In this episode, I talk again with Marcin Grzejszczak on this topic and we discuss how the newly GA's Spring Cloud Contract enables all this thinking. See https://blog.pivotal.io/pivotal-conversations for full show notes.

We’ve all heard the term tree huggers. If you were around during the early days of server virtualization, you may remember the phenomenon of server huggers. These were the people who were adamant that their systems and applications were too important to move to virtual servers, clinging to their physical servers for dear life. The latest “hugger” variant is the data hugger - those folks that don’t want to share their data with other departments or data science groups to fill the so-called data lake to support data science and analytics. In this episode of Pivotal Insights, host Jeff Kelly talks with Dormain Drewitz, head of product marketing for Pivotal’s data business, contemplating the data hugger mindset and propose some strategies for changing hearts and minds when it comes to sharing data. The two also chat about Dreamforce - the massive Salesforce customer conference that descended on San Francisco last week - and Salesforce’s move to embed artificial intelligence into its eponymous SaaS CRM application and the wider implications for the blending of insights with applications.

Building a high performance organization requires more than just putting good technologies and practices in place for developing and delivering product, it requires the right culture as well. In large organizations, this often means changing the culture. At the heart of that is people, so it's natural that Human Resources will get involved, hopefully sooner rather than later. To discuss these topics, we bring back Joe Militello for the second time to discuss how Pivotal thinks through HR and the consultative work our team has been doing on these topics. His framing that I really liked relates to his summary of what HR does: improving "the experience of our employees and candidates.” We go over some best practices for transforming how HR operates and give a little peek into how Pivotal manages employee's experience.

Released a few weeks ago, Pivotal Cloud Foundry 1.8 is chock full of new features and improvements. We talk with Jared Ruckle about them, delving into security, databases, and new services. These features deliver on the Pivotal Cloud Foundry goal of speeding up time to market (with faster release cycles) and, yet, still being a general purpose application platform that organizations can use to run all their customer software. We also discuss another recent piece from Jared on opinionated platforms - check out that tree house! In the news, we cover the recent data breach at Yahoo, Windows Server 2016 and Docker support, Azure's ever growing geographic foot-print, and our hopes and dreams for the rumored Twitter acquisition. See full show notes: http://pivotal.io/podcast

Backed up into a corner, developers will start coding. It's little wonder then that as large organizations have been faced with modernizing their approach to software - all that "digital transformation" - developers in years past have been focusing on building their own platforms. Our guest this week, Matt Walburn, worked on one such project. He joins us this week to talk about the lure of the DIY platform and why, now that options like Pivotal Cloud Foundry are available, it's usually a poor use of organization time. Not only do you need to build the full platform with all the features from the development phase to running in production, but you have to maintain it as well. As Matt says, this will run you several millions of dollars in staff salary alone. And then, after all that, you still have to write all those applications you originally set out to make. See full show notes at http://pivotal.io/podcast

No matter how fresh and new your company is, you're going to have some "legacy" applications to work with when you're mounting your cloud native efforts. The nature of those legacy apps and services are varied: mainframes, ESBs, batch job, and plain old J2EE and .Net apps. If you find yourself unable to make changes quickly enough without the fear of it all blowing up in your face, you're probably dealing with legacy. Pivotal's Rohit Kelapure talks with us in this episode about the type of analysis and, then, types patterns he and his team use to "break up the monolith." Before all that we discuss some recent news: HPE selling off its software group, Google buying Apigee, Richard and Abby's recent commentary on the container market, and fresh coiffure advice for listeners. Visit https://blog.pivotal.io/pivotal-conversations/ for show notes and other episodes.