March 27, 2013 · 6:00 PM
We're merging this event with the "How to Find the Golden Nugget in Big Data" event happening on the 27th at VMware.
6:00 pm – 6:30 pm: Registration & Networking
6:30 pm – 7:00 pm: First Use Case
7:00 pm - 7:30 pm: Twitter Use Case: Real Time Analytics
7:30 pm - 8:00 pm: Q&A
In our first meetup we explored the definition of Big Data, and in our second meetup we’re diving into big data analysis to find meaningful and actionable data among the data being collected. This could be tens of thousands to millions of transactions per second. Finding that "golden nugget" can prove more challenging than most people expect. We'll be exploring two use cases of companies looking at their data to understand their customers better and provide a better user experience.
You will also learn how to build a Twitter-like analytics system, designed to meet real time needs, in a simple way. Using frameworks such as Spring Social, Active In-Memory Data Grid for Big Data event processing, and NoSQL database.
• Hadoop's batch-oriented processing is sufficient for many use cases, especially where the frequency of data reporting doesn't need to be up-to-the-minute. However, batch processing isn't always adequate, particularly when serving online needs such as mobile and web clients, or markets with real-time changing conditions such as finance and advertising.
• In the same way that Hadoop was born out of large-scale web applications, a new class of scalable frameworks and platforms for handling streaming or real time analysis and processing is born to handle the needs of large-scale location-aware mobile, social and sensor use. Do we want to limit ourselves to just these use cases?
• Facebook, Twitter and Google have been pioneers in that arena and recently launched new analytics services designed to meet the real time needs.
We will review the common patterns and architecture that drive these platforms and learn how to build a Twitter-like analytics system in a simple way using frameworks such as Spring Social, Active In-Memroy Data Grid for Big Data event processing, and NoSQL database such as Cassandra or Hbase for handling the managing the historical data.