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    Blogs

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    MICROSOFT AND ORACLE CLOUD PARTNERSHIP FOR AZURE

    Microsoft announced support for Big Data on Azure platform on 12th October 2011. The exposed REST services/interface can be leveraged by developers to submit and execute MR jobs. Javascript based interface provides GUI based environment and can be used as an alternate along with Hadoop command prompt. The same environment can easily be integrated with existing BI tools (Power BI, MS Excel) and allows HIVE connectivity using ODBC Drivers. Following are two additional connectors that allows connectivity to access structures/unstructured data:

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    EXPANDING BUSINESS USING AZURE

    Microsoft announced Windows Azure Platform in October 2008 and joined cloud service providers list. The contribution was invaluable and shaped many different aspects in very short duration and became commercially available on 1st Feb 2010. Enduring the promising provisions Windows Azure was ranked as first (First in Cloud Speed Test conducted by Compuware.

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    CROWDSOURCING OR INNOVATION OR BOTH?

    Innovation through crowdsourcing is not new but proven to be one of the most impressive techniques to innovate and improvise. Check an interesting study shared from blogs.technet.com. The Power of Crowdsourcing: Why the Next Best Big Idea for Your Business May Be in the Brain of a 14-Year-Old in Belgium from Visit us at datopic, share your business ideas or connect to join datopic ride on data science and cloud and take deep dive into big data solutions.

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    HADOOP ON MICROSOFT AZURE

    Microsoft announced support for BigData on Azure platform on 12th October 2011 (News). Launched platform deliver 100% Apache Hadoop based distribution installed on Windows Servers. Distribution comprises of HDFS, Hive, PIG installation and available as hadoop-1.1.0-SNAPSHOT under HDInsight 1.6. The exposed REST services/interface can be leveraged by developers to submit and execute MR jobs. JavaScript based interface provides GUI based environment and can be used as an alternate along with Hadoop command prompt.

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    UPLOADING DATA TO BLOB STORAGE

    One of the key requirement for every business considering Hadoop on cloud is “How to upload data to BLOB”. The answer lies at the size of the data and time you want to invest during uploading process. It may also vary on the basis of activity you perform. For example whether you want to put all the data at once or prefer to perform uploading it in chunks followed by action to perform same activity multiple times or in-parallel. With all so many variation end result would be to have data available on cloud.

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    BIG DATA AND CRICKET 2015

    Its less than a month away from biggest cricketing spectacle on earth, the ICC World Cup 2015. Everyone is excited and sites tighten up to share scores, news, headlines, schedules & results. On other hand broadcasters have pushing hard to provide maximum coverage on globe and relay best quality for their audiences. No doubt the whole event is going to be lot of fun and emotional. It is said to be another religion and add lot of noise sooner

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    TOP BIG DATA PAPERS

    Following are list of papers responsible to change BIG DATA trends: MapReduce: Simplified Data Processing on Large Clusters The Google File System Bigtable: A Distributed Storage System for Structured Data Dynamo: Amazon’s Highly Available Key-value Store The Chubby lock service for loosely-coupled distributed systems Chukwa: A large-scale monitoring system Cassandra – A Decentralized Structured Storage System

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    NEXT GENERATION INDIAN RAILWAY E-TICKETING WITH BIGDATA

    With India’s massive online population growth, the e-ticketing systems supporting India Rail faced a major challenge with scale. At a recent forum in India, the team responsible for the applications explained how they overcame the scale and other issues with Pivotal GemFire, tripling application throughput. With a country-wide responsibility for making e-ticketing work for its citizens, India Rail relies on Pivotal GemFire to help them address the future as internet users increase, new applications connect, and functionality expands to support consumers.

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    DATA ANALYTICS DATA STRUCTURES

    After a decade of programming around normalized or structured data structures, era of big data moved focus back to unstructured data structures and further divided it into sub parts. Depending upon different data structures industry have offered different techniques to deal with. Different Data Structures:

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    MICROSOFT RELEASES AZURE HDINSIGHT ON LINUX AND MACHINE LEARNING GA

    Yesterday at Strata + Hadoop World, Microsoft is announcing the public preview of Azure HDInsight running on Linux, the general availability of Storm on HDInsight, the general availability of Azure Machine Learning, and the availability of Informatica. http://blogs.technet.com/b/dataplatforminsider/archive/2015/02/18/microsoft-releases-azure-hdinsight-on-linux-and-machine-learning-ga-at-strata-conference.aspx

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