5 Big Data stories to read this weekend

As we approach the weekend, probably take a break from the heavy technical stuff to read more about the buzz that Big Data has been generating along. Listed below are 5 hand-picked Big Data stories (in Om Malik way) that you would enjoy reading over the weekend…

Clearing Up Some Confusion on theHadoop Wars

Gives a fair perspective on the Cloudera and Hortonworks strategy on Hadoop – though the post paints more positive picture of Hortonworks and remains silent about MapR, another key player.

Who doesn’t love Hadoop?

Carrying a similar tone, this post puts a perspective on why Hadoop is still open source and even the likes of Microsoft joining this OSS ecosystem.

Cassandra @Twitter: An Interview with Ryan King

A short crisp discussion with Ryan King of Twitter on why Cassandra is ruling the roost in Tweet world.

10 Big Data Sites to Watch - By Gil Press | ForeignPolicy

A good list of 10 sites which are using Big Data for competitive advantage – though not with Hadoop and usual tools, but still good use cases to think over

Managing sewage like traffic thanks to data

And, a real life example of how Big Data could be used for basic amenities like managing sewage in city. Perfect example of smarter city as well a guiding example for Big Data usage.

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In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB. Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive.
Introducing Apache Gora Although there are var…

Amazon DynamoDB datastore for Gora

What was initially suggested during causal conversation at ApacheCon2011 in November 2011 as a “neat idea”, would soon become prime ground for Gora's first taste of participation within Google's Summer of Code program. Initially, the project, titled Amazon DynamoDB datastore for Gora, merely aimed to extend the Gora framework to Amazon DynamoDB. However, it seem became obvious that the issue would include much more than that simple vision.

The Gora 0.3 Toolbox We briefly digress to discuss some other noticeable additions to Gora in 0.3, namely: Modification of the Query interface: The Query interface was amended from Query<K, T> to Query<K, T extends Persistent> to be more precise and explicit for developers. Consequently all implementors and users of the Query interface can only pass object's of Persistent type. Logging improvements for data store mappings: A key aspect of using Gora well is the establishment and accurate definitio…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

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