Stream Conf 2016, which covers the technologies, architectures and business strategies for the streaming web, will be held in San Francisco on September 28, 2016, at the
Bently Reserve in San Francisco. The event provides insights into learning how always-on data streams can be managed, scaled, secured and monetized.
The agenda for Stream Conf 2016 includes tracks, sessions and workshops with speakers from Cisco, Iron.io, Microsoft, Mozilla, PubNub, Texas Instruments. Topics will include:
Architecture: How Data Stream-based Applications Are Driving a New Software Stack
As software evolves to an “always on” data stream architecture, existing three-tier software architectures are being stretched to the limit to handle the latency, reliability and bandwidth requirements of data realtime applications and connected devices. Discussion will include new architecture designs necessary for data stream-driven technologies, what standards and protocols that need to emerge, and how data streams will impact everything from business logic and data processing to security and analytics.
Security: Challenges and Strategies for Data Stream-centric Security
Most of today’s security models revolve around systems, networks, and people. But data streams and data stream networks provide a new opportunity for addressing security at the data stream itself. Included will be discussions and panels that explore how to secure data streams and what security models work best.
Business Models: Strategies to Fund and Grow Data Stream-driven Businesses
Requirements for live, streaming data are driving big changes ahead. What challenges and opportunities will this present? Leaders in the industry give their perspectives and make predictions on how data streams shape business models and technology strategies, what monetization opportunities data streams are opening up and innovative investment funding strategies that lie ahead.
Successes and Failures: Real-world Experience in Building and Scaling Data Stream-based Applications
Hear first-hand battle stories from companies that have built data stream-based apps, architectures and business models. What’s worked, what hasn’t and why.