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About HadoopSphere

HadoopSphere is an initiative by Sachin Ghai to provide a community driven platform for exchanging free and frank thoughts on big data technologies. Sachin is a key innovator and futuristic who works on building massive scalability systems with current interests in intersection of cloud, big data and artificial intelligence. To get in touch with Sachin, send an e-mail to scale@hadoopsphere.com or send a message on Twitter.
All views on HadoopSphere are in individual capacity and bear no endorsement from the organizations Sachin has been associated with. All effort has been taken to avoid any conflict of interest in any article.


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The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing. Apache Hadoop is an independent project run by volunteers at the Apache Software Foundation.

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Technical deep dive into Apache Tajo

Over the past few months, a new Hadoop based warehouse called Apache Tajo has been making the buzz. We tried to do a technical deep dive in the topic and reached out to PMC chair Hyunsik Choi. Apache Tajo is an Apache top level project since March 2014 and supports SQL standards. It has a powerful distributed processing architecture which is not based on MapReduce. To get more sense on Tajo's claims for providing a distributed, fault-tolerant, low-latency and high throughput SQL engine, we asked a few questions to Choi. This Q&A is published in a two part article series on hadoopsphere.com. Read below to get a better idea on Apache Tajo.
How does Apache Tajo work? As you may already know, Tajo does not use MapReduce and has its own distributed processing framework which is flexible and specialized to relational processing. Since its storage manager is pluggable, Tajo can access data sets stored in various storages , such as HDFS, Amazon S3, Openstack Swift or local file system. …

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.
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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…