Startup Streamlio Inc. is betting that organizations are ready for real-time streaming architectures to process their basic data needs, and now it has brought three of the latest open-source technologies to bear on the process.

The company’s new real-time analytics suite incorporates the Apache Pulsar publish-and-subscribe engine with Heron, a real-time, distributed, fault-tolerant stream processing engine originally developed at Twitter Inc. and Apache BookKeeper, a low-latency storage service designed for real-time workloads. The combination is “the only enterprise-grade messaging solution optimized for streaming and storage,” said Streamlio co-founder and Chief Executive Lewis Kaneshiro.

Working across clouds can be a tricky business. A company’s clouds might come from different providers, run in private or in public, or just be highly customized to work with mission-critical legacy applications.

The latest release of the OpenStack cloud platform landed on Aug. 30 with the debut of OpenStack Pike. While there are many incremental feature improvements in Pike, there is at least one key feature noteworthy in that it hasn't been removed. That feature is Amazon Web Services (AWS) API compatibility.

Though the OpenStack Foundation and its member companies rarely talk about AWS compatibility, it's a feature OpenStack has long supported.

Apache Kafka is a distributed publish-subscribe messaging system designed to be fast, scalable, and durable. It provides a unified, high-throughput, low-latency platform for handling real-time data feeds and has a storage layer that is essentially a massively scalable pub/sub message queue architected as a distributed transaction log. That architecture makes Kafka, which was originally developed by LinkedIn and made open source in early 2011, highly valuable for enterprise infrastructures to process streaming data.

Originally, Kafka was built for website activity tracking—capturing all the clicks, actions, or inputs on a website and enabling multiple "consumers" to subscribe to real-time updates of that information. Now, however, companies in internet services, financial services, entertainment, and other industries have adapted Kafka's massively scalable architecture and applied it to valuable business data.

Jim is a powerhouse of knowledge in open source. He is fully versed in technical issues and deeply experienced in legal matters, both visible immediately in his quick, easy and comprehensive commentary around virtually any open source-related subject. Our discussion was framed by the same three questions as all the others in season one: how did he enter open source? what was the most interesting thing he observed? what did he think we should keep our eyes open for in the next 12 to 24 months? What stood out is perhaps how Jim tied his answers to the longer history of open source itself, and framed his answers in the content of our 20 plus year evolution as a community.

One thing that Jim’s interview highlighted was that there was plenty of scope for deeper, more comprehensive interviews as we explored open source law, and he helped set the tone that would see a decision to shoot long-form interviews in the forthcoming series two.

The 3rd day should have started with a Debian sprint and then a LibreOffice one, taking advantage I’m still attending, as that’s my last day. But plans don’t always work out and we started 2 hours later. When everybody arrive we got everyone together for a short daily meeting (scrum style). The people were divided to 3 teams for translating:  Debian Installer, LibreOffice and Gnome. For each team we did a short list of what left and with what to start. And in the end – how does what so there will be no toe stepping. I was really proud with this and felt it was time well spent.

Healthcare data analysts frustrated by the lack of access to large volumes of clean, trusted, and complete patient data can now take advantage of an open source EHR data generator platform called Synthea.

One million synthetic patient records are currently available within the free online system, which uses HL7 FHIR to allow access to standardized datasets that mimic real electronic health records. 

The wealth of easily accessible data may be a boon for the growing fields of machine learning and artificial intelligence, which require access to significant amounts of big data in order to train for clinical decision support, predictive analytics, and other patient care applications.

Any organization using open source source software should make sure there is a strong open source policy in place that dots the "I"s and crosses the "T"s. Why? Because open source licenses recently became even more enforceable than they were already.

For some time case law has made them enforceable in copyright cases. Now, with a federal district court ruling in Artifex vs. Hancom, they're clearly enforceable in contract disputes as well.

First a little history.

For a long time after Richard Stallman penned the first General Public License in 1989, there was a cloud with a question mark inside hanging over all open source projects. No one knew whether the license would be legally enforceable. When consulted, legal eagles were pretty much in agreement that they should be enforceable -- with the caveat that lawyers and judges have sometimes disagreed on seemingly clear points of law. Until there was a court case centered on their enforceability, they said, open source projects were in limbo.

On releasing modified versions of GPLv3 software in binary form only...