Bargava Subramanian and Harjinder Mistry share data engineering and machine learning strategies for building an efficient real-time recommendation engine when the transaction data is both big and wide. They also outline a novel way of generating frequent patterns using collaborative filtering and matrix factorization on Apache Spark and serving it using Elasticsearch in the cloud.

Join us for the NANOG 67 Hackathon – a one-day event Sunday, June 12, 2016, at the Fairmont Chicago Millennium Park. The NANOG 67 Hackathon will bring network ope …

Drawing on examples from Adidas, Kristian Skoeld explains how to create a data-driven third-party governance strategy that delivers greater insights into frontend performance and continuous delivery.

FINRA ingests over 50 billion records of stock market trading data daily into multipetabyte databases. Janaki Parameswaran and Kishore Ramachandran explain how FINRA technology integrates data feeds from disparate systems to provide analytics and visuals for regulating equities, options, and fixed-income markets.

While other industries have embraced the digital era, healthcare is still playing catch-up. Kaiser Permanente has been a leader in healthcare technology and first started using computing to improve healthcare results in the 1960s. Taposh Roy, Rajiv Synghal, and Sabrina Dahlgren offer an overview of Kaisers big data strategy and explain how other organizations can adopt similar strategies.

Common ground, an important concept in recent teamwork research, helps us understand why collaborative troubleshooting breaks down over time, leading to wasted effort and mistakes. Drawing on common ground as well as some ideas from medical diagnosis, Dan Slimmon demonstrates that by extending ChatOps, we can make troubleshooting much easier without losing the benefits of fluid team conversation.

Federated analytics, a new approach to analyzing big data, supports unprecedented collaboration across large distributed datasets that contain proprietary and/or protected information. Gilad Olswang explains how Intel harnesses the power of federated analytics in the Collaborative Cancer Cloud project.

Dieter Reuther and Donna Denio demonstrate a universal language that helps you maneuver through complex leadership challenges. Using a business case study, they guide attendees through the Lego Serious Play methodology to explain how to break down situations into simple 3D models. This is an interactive session with audience participation. Each attendee will get their own Lego set.

Increasing your online presence in the open source community involves more than just writing and committing code. We need to create safe places to collaborate and communicate freely in order to involve more people than just our closest team members. Jonas Rosland and Stephanie Carlson outline a few of the tools they use to tackle this big task and discuss failures, successes, and lessons learned.

Open source efforts have proved to the world that collaborative development creates and innovates at speed and scale. Companies now know that using open source software is table stakes to be economically competitive. Danese Cooper explains how to ensure that the communities that created these opportunities continue to grow, develop, and inspire more new communities.

Everyone is talking about microservices, but there is more confusion than ever about what the promise of microservices really means and how to deliver on it. Jonas Bonr explores microservices from first principles, distilling their essence and putting them in their true context: distributed systems.

If you have a roadmap that no one uses, it may be an anemic roadmap. An anemic roadmap is just a list of features that goes on as far as the eye can see. This is harmful to your organization. John Heintz explains how to build truly useful roadmaps that actually help us define goals and work jointly by weaving together marketing, product, architecture, and more.

Jayant Shekhar, Amandeep Khurana, Krishna Sankar, and Vartika Singh guide participants through techniques for building machine-learning apps using Spark MLlib and Spark ML and demonstrate the principles of graph processing with Spark GraphX.