Pivotal Insights

There are a number of capabilities developers need in order to be successful. One of them is easy and fast access to data. Today more than ever, applications rely on both large scale and high-velocity data pipelines to support key features and capabilities. But with so many applications and microservices requiring data, things can get pretty complex pretty quickly. There needs to be a way to tame this complexity so developers can do their best work. In this week's episode of Pivotal Insights, hosts Jeff Kelly and Dormain Drewitz speak with Shawn McAllister, CTO at Solace Systems. Solace is a provider of open data movement solutions and a Pivotal partner. The three discuss some of the challenges developers face building data pipelines and messaging queues in cloud-native environments, highlight some speccific use cases like data movement for IoT applications, and explore how developers can leverage Solace on Pivotal Cloud Foundry.

Turns out there's a lot more to building a strong partner ecosystem that delivers real value to customers than just putting out joint press releases with lovey-dovey language, a.k.a. Barney press releases. Luckily, that's the type of work Pivotal is putting in with partners across the Cloud Foundry ecosystem. In this episode of Pivotal Insights, Dormain and Jeff talk about what it means to put customers first and the importance of a vibrant ecosystem. They also discuss enterprises making the transition to software-driven. As an added bonus, Dormain shares some insights on ancient Roman festivals.

One of the fundamental tenants of twelve-factor applications is that developers shouldn’t have to concern themselves with the underlying complexities of the infrastructure supporting their apps. This goes for dealing with how 12 factor apps communicate with each other. Spring Cloud Stream helps mitigates this challenge, whether you’re using RabbitMQ, Kafka, Google pub/sub or any other messaging tool. “With Spring Cloud Stream, the messaging middleware will not behave the same, but your application will work the same,” said Marius Bogoevici, the project lead for Spring Cloud Stream at Pivotal. In this episode of Pivotal Insights, host Jeff Kelly speaks with Marius about the challenges associated with developing message-driven microservices in cloud-native environments, how Spring Cloud Stream lets developers focus on building software that benefits the business, and how Pivotal customers are putting Spring Cloud Stream to work in the enterprise.

Microservices provide a number of benefits to developers and, ultimately, end-users of applications supported by microservices architectures. These include the ability to scale and develop each microservice independently, which enables increased release velocity. But microservices also introduce a number of challenges that developers need to address, especially if they are developing mission-critical transactional applications. Among these challenges is maintaining data consistency across a set of microservices. Enter command query responsibility segregation, or CQRS. In this episode of Pivotal Insights, host Jeff Kelly dives into the topic of CQRS with Pivotal's own Kenny Bastani. Kenny, a Spring developer advocate, lays out the challenges CQRS helps microservices developers overcome, provides advice for implementing CQRS, and discusses the complexities that CQRS itself introduces to microservices applications.

There was a time when the only people concerned with application performance were operations teams. And maybe the enterprise users frustrated by slow app performance. But today, anyone interested in the the performance of their business should be interested in the performance of their applications, says Abner Germanow. Germanow, who is Senior Director of Strategic Marketing and Campaigns at New Relic, says that now more than ever application performance has a direct impact on customer interactions, revenue generation and the overall performance of the business. In this week's episode of Pivotal Insights, host Jeff Kelly and Germanow discuss the evolving role of application performance monitoring (APM) in cloud-native environments, the impact of microservices and DevOps on APM, how data is the great equalizer in ensuring application performance, and how Pivotal and New Relic are helping joint customers keep their cloud-native applications humming.

When you think about microservices, you probably think of open-ended services supporting user facing applications like Netflix or Uber. These services must be "always-on." There's no finish line. Some call these streaming services. But not all microservices fall into this category. Some microservices are finite - they have a beginning and a definitive end. Microservices supporting batch data integration jobs fall into this category. So do those supporting database migrations. These microservices shut down after the job is accomplished. Finite microservices like these have their own set of development and deployment requirements and challenges that set them apart from streaming microservices. In this episode of Pivotal Insights, host Jeff Kelly speaks with Michael Minella, project lead for Spring Cloud Task at Pivotal. The two discuss what differentiates finite microservices from their streaming counterparts, identify the unique challenges associated with developing and deploying them, and offers tips for overcoming these challenges with Spring Cloud Task.

In this episode of Pivotal Insights, Pivotal’s Sina Sojoodi talks with host Jeff Kelly about how Pivotal clients across industries are turning to Spring Cloud Data Flow (SCDF) to do just that. SCDF is an orchestration tool that enables developers to create composable microservices, including streaming and batch data pipelines, and deploy them on the run-time environment of their choice. Sina, the project lead for Spring Cloud Data Flow at Pivotal, and Jeff discuss the tangible benefits SCDF delivers to developers, including improving productivity, and share examples of the types of microservices Pivotal customers are developing and orchestrating with SCDF.

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

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.

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.

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.

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.

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.

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.

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.)