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This week, we talk with Jared Ruckle about the new Pivotal Cloud Foundry 1.8 release, delving into security, databases, and new services—all aimed at speeding up time to market (with faster software release cycles) and, yet, still being a general purpose application platform that organizations can use to run all their customer software. Also covered: the recent data breach at Yahoo!, Windows Server 2016 and Docker support, Azure's ever growing geographic foot-print, and the rumored Twitter acquisition.
For the most part, Java developer's favorite thing to do with Microsoft was to ignore it. Closed source, high price tags and proprietary interfaces only strengthened arguments for Java developers to keep Microsoft out of their projects. Many Java gurus may have stopped paying attention to Microsoft all together. If you did, you may have missed some of their latest efforts and progress. Check it out—it might even be enough to reconsider Microsoft's reputation entirely.
Apache MADlib includes new utilities that make it easier for data scientists to shape and transform data, and to evaluate the accuracy of predictive models. It is well known that data scientists can spend an inordinate amount of time on data wrangling tasks, which takes time away from model design and development. The new MADlib utilities—Pivot, Sessionization, and Prediction Metrics—described in this article are designed to help reduce this burden.
Pivotal and Microsoft are working hard to make the cloud a friendlier place for .NET developers. This post describes five major advancements we’ve made in partnership with Microsoft over the past few months, including bringing cloud native patterns into your .NET code with Steeltoe, consistently deploying ASP.NET Core apps using the new buildpack, running applications in Windows Server 2016 containers, taking advantage of the Microsoft cloud by running Pivotal Cloud Foundry on Microsoft Azure, and simplifying Windows lifecycle operations using BOSH.
This week, hosts Coté and Richard Seroter interview Matt Walburn, who recently worked on a DIY platform project. He talks 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. 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.
The Cloud Foundry community often proudly proclaims a key part of its current success and future lies in the fact that it is an opinionated platform. But what is an “opinionated” platform? The existential questions of how to deploy to the platform and run apps on the platform are answered in a specific, if not rigid, way to boost productivity. This post discusses the benefits of operating with an opinionated platform.
In this post, Charles Killam, Pivotal Principal Technical Instructor, explains how data partitions work in Pivotal Greenplum Database. Often, default partitions offer convenience, but they don’t perform nearly as well as alternative options. When designing a solution, architects should consider several alternatives which will greatly increase performance.
Pivotal Data Scientist Chris Rawles shares a useful example of how to operationalize a text analytics model and deploy it as a scalable, autonomous microservice on Cloud Foundry. This example demonstrates an approach to deploying a scalable trained sentiment classifier that can also conveniently be used for additional text analytics and other data science tasks. Code samples are provided on Github.
The new Pivotal Cloud Foundry 1.8 release delivers more power and flexibility to improve the critical measure of time to value when delivering software. The release adds TCP routing to support non-HTTP/S workloads - now more legacy systems, IoT services, and containerized apps can be run on Pivotal Cloud Foundry. It also introduces service networks for operations teams, improved security features, and log-integrated metrics & monitoring.
No matter how fresh and new your company is, dealing with "legacy" applications is inevitable. 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. This week, Pivotal's Rohit Kelapure talks with us about the type of analysis and, then, types patterns he and his team use to "break up the monolith."