Lambda Architecture: Achieving Velocity and Volume with Big Data 4

Lambda Architecture: Achieving Velocity and Volume with Big Data
Big data architecture paradigms are commonly separated into two (supposedly) diametrical models, the more traditional batch and the (near) real-time processing. The most popular technologies representing the two are Hadoop with MapReduce and Storm. However, a hybrid solution, the Lambda Architecture, challenges the idea that these approaches have to exclude each other. The Lambda Architecture combines ... read more →

GraphChi: How a Mac Mini outperformed a 1,636 node Hadoop cluster

GraphChi: How a Mac Mini outperformed a 1,636 node Hadoop cluster
Last year GraphChi, a spin-off of GraphLab, a distributed graph-based high performance computation framework, did something remarkable. GraphChi outperformed a 1,636 node Hadoop cluster processing a Twitter graph (dataset from 2010) with 1.5 billion edges – using a single Mac Mini. The task was triangle counting and the Hadoop cluster required over 7 hours while ... read more →

32x Faster Hadoop and MapReduce With Indexing 8

32x Faster Hadoop and MapReduce With Indexing
Hadoop and map reduce’s simplicity, and especially lack of indices, significantly limits its performance. I described how map reduce 2.0 and alternatives bypassing map reduce will change Hadoop’s application and speed it up in the next year or two. Another approach is the introduction of indices to data stored on Hadoop Distributed File System (HDFS). At its inception, ... read more →