How do you go about building a product around data using Hadoop?
This talk will present how LinkedIn builds and maintains such
features as People You May Know. We will present our architecture
for doing so (open-sourced) as well as knowledge we've gained in
A discussion of Big Data approaches to analysis problems in marketing, forecasting, academia and enterprise computing. We focus on practices to enhance collaboration and employ rich statistical methods: a Magnetic, Agile and Deep (MAD) approach to analytics. While the approach is language-agnostic, we show that sophisticated statistics can be easily scaled in traditional environments like SQL.
Most analytics systems rely on large offline computations, which means results come in hours or days behind. Twitter is all about realtime, but with over 160 million users producing over 90 million tweets per day, we need realtime analytics that scaled horizontally. This talk discusses the development of that infrastructure, as well as the products we are beginning to build on top of it.
To many people, Big Data means Open Data: social graphs, voting records, weather patterns, and more. But who owns data? Most of our laws were written for atoms, not bits; they're woefully out of date in an information age. When you share data, does it become more or less valuable? If someone adds to your data, is it still yours? This panel will tackle the gray area of data ownership.
Two forces define the big data era: size and speed. These forces are driving companies to consider new choices for how they deal with data.. Read more