analytics
Making Hadoop Run Faster One of the challenges in processing data is that the speed at which we can input data is quite often much faster than the speed at which we can process it. This problem becomes even more pronounced in the context of Big Data, where the volume
nosql
This is the third guest post (part 1, part 2) of a series by Greg Lindahl, CTO of blekko, the spam free search engine. Previously, Greg was Founder and Distinguished Engineer at PathScale, at which he was the architect of the InfiniPath low-latency InfiniBand HCA, used to build tightly-coupled supercomputing
nosql
Major social networking platforms like Facebook and Twitter have developed their own architectures for handling the need for real-time analytics on huge amounts of data. However, not every company has the need or resources to build their own Twitter-like solution. In this example we have taken the same Twitter/Facebook-like
Strategy
The EuroSys 2012 system conference has an excellent live blog summary of their talks for: Day 1, Day 2, Day 3 (thanks Henry at the Paper Trail blog). Summaries for each of the accepted papers are here. One of the more interesting papers from a NoSQL perspective was Cache Craftiness
nosql
Edd Dumbill wrote an interesting article on O’Reilly Radar covering the current solutions for running Big Data in the Cloud Big data and cloud technology go hand-in-hand. Big data needs clusters of servers for processing, which clouds can readily provide. Big PaaS Edd touched briefly on the role of
Example
After the filesystem and simple library packages like dbm, Berkeley DB was the original luxury embedded database widely used by applications as their core database engine. NoSQL before NoSQL was cool. The hidden secret making complex applications sing. If you want to dispense with all the network overhead of a
Hadoop
This is a guest post by Doug Judd, original creator of Hypertable and the CEO of Hypertable, Inc. Hypertable delivers 2X better throughput in most tests -- HBase fails 41 and 167 billion record insert tests, overwhelmed by garbage collection -- Both systems deliver similar results for random read uniform
nosql
This is a guest post by Siddharth Anand, a senior member of LinkedIn's Distributed Data Systems team. Preamble Ramble If you’ve been working in the online (e.g. internet) space over the past 3 years, you are no stranger to terms like “the cloud” and “NoSQL”. In
Strategy
In the "should I or shouldn't I" debate around deploying SSD, it always helps to have real-world data. Fiesta! with a live-blog summary of a presentation by Kenny Gorman on Shutterfly on MongoDB Performance Tuning. What if you still need more performance after doing all of
architecture
The InfoQ article Finding the Right Data Solution for Your Application in the Data Storage Haystack makes a series of concrete recommendations for a user who wants to find the right storage solution for his application. Few years back, there was a time SQL RDBMS were solution for almost all
Strategy
Jared Rosoff concisely, effectively, entertainingly, and convincingly gives an 8 minute MongoDB tutorial on scaling MongoDB at Scale Out Camp. The ideas aren't just limited to MongoDB, they work for most any database: Optimize your queries; Know your working set size; Tune your file system; Choose the right
scalabilty
It's time to think of the architecture and application platforms surrounding "Big Data" databases. Big Data is often centered around new database technologies mostly from the emerging NoSQL world. The main challenge that these databases solve is how to handle massive amount of data at a