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by Tom Hanlon
Hadoop gives you the ability to process massive amounts of data at scale. This presentation will show you how hadoop makes use of commodity hardware to allow you to build a system that scales, that deals gracefully with failure of individual nodes, and gives you the power of Map/Reduce to process Petabytes.
Ever had to dig into a system that misused the most basic features of a RDBMS ? Better yet - after the whole NoSQL storm had you wondered why it didn't shown before when you had to twist your schema to fit into something it was not designed for ? Check on this anti-patterns collection and feel better that you are not alone - and how you can benefit from it even not having big data around.
by Ted Dziuba
What happens when you write data to disk? We'll explore everything between your programming language and the spinning platters - both optimizations and dangerous pitfalls.
The art of dealing with real-time data is not new. In fact, much of the world's economy is propped up my making decisions on data sub milliseconds. The technology is there, we have the power. We'll take a whirlwind tour of the open-source Esper system and understand how to integrate it into your stack to enable rapid decision making on real-time data from anywhere in your architecture.
An overview of the state of the art for bringing together the analytical power of the R language with the big data capabilities of Hadoop.
We produce gorgeous LaTeX reports while harnessing the power of R on the backend. The data is pulled from our PostgreSQL database, the analysis and visualizations are fast and distributed thanks to Redis. We'll talk about weaving together open source tools to build powerful analytics reporting engines that rival the commercial alternatives.
PostgreSQL continues to provide a major release every year full of improvements, better performance and features that measure up to the most popular commercial databases. Our 2011 release, 9.1, is no exception!
In November, Facebook launched a new version of Messages that combines chat, SMS, email, and Messages into a real-time conversation. Facebook relies on Apache HBase, a NoSQL-style database, for storing this real-time message data. This talk will elaborate on our decision process, system configuration, scaling issues, and advantages gained by choosing Open Source.
Keeping a busy site going when you don't have a lot of servers or developer resources can be a struggle. Hear what we did at Daily Kos to make the most of what we had to bring MySQL in line, make it quick, and keep the users and the boss happy.
Building large data applications can present a unique set of technical challenges because things that often work well in the conventional development environment can become incredibly arduous or expensive when applied on a much bigger scale. This talk will cover some of those challenges and potential solutions for each.
This language-agnostic proposal focuses upon concepts and strategies critical to the design and implementation of asynchronous systems and data processing layers. Key components include a survey of implementation strategies for non-blocking edge tiers, patterns for building out a distributed worker / processing tier, along with several horror stories of cascading failures and their resolution.
Time Series sensors are being ubiquitously integrated in places like cell phones, environmental sensors, and the smart grid. As we scale out this type of data RDBMS systems strain to scale with the high insertion rates and real time query requirements. In this talk we introduce “Lumberyard” which is a scalable indexing and low latency fuzzy pattern searching time series data.
25th–27th July 2011