{"id":19,"date":"2012-08-24T12:49:18","date_gmt":"2012-08-24T19:49:18","guid":{"rendered":"http:\/\/www.riverlog.com\/wordpress\/?p=19"},"modified":"2012-08-24T12:49:18","modified_gmt":"2012-08-24T19:49:18","slug":"big-data-the-big-players","status":"publish","type":"post","link":"https:\/\/riverlog.bigleafproductions.com\/wordpress\/2012\/08\/big-data-the-big-players\/","title":{"rendered":"Big Data & The Big Players"},"content":{"rendered":"

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Is it David & Goliath or Goliath and David?<\/p>\n

Big data has been gaining more popularity in the TAG world and in the world of synonyms for some time now. <\/em>More innovation has always been seen within small organizations. With Hadoop being accepted among the developer community, with a considerable amount of learning curve, \u00c2\u00a0it is important that all involved in \u00e2\u20ac\u0153drilling\u00e2\u20ac\u009d down on any kind of voluminous data, give importance to \u00e2\u20ac\u0153Hadoop\u00e2\u20ac\u009d. Now, Sqoop\u00e2\u20ac\u2122s entry has helped enterprises in thinking more about their disparate databases. In database where one field embeds many other information such as \u00e2\u20ac\u0153outcome\u00e2\u20ac\u009d information of any actions, retrieving those information, sunk down the deep caves of enterprises \u00c2\u00a0repositories is becoming less and less of an issue. More thoughts and joint thinking has to be inputted with the realms of what to do with the data or simply , one has to starting thinking of \u00e2\u20ac\u0153How to make sense of this data\u00e2\u20ac\u009d.<\/p>\n

Large corporations such as Oracle ,Google, \u00c2\u00a0IBM and other companies have begun brining out tools for the \u00e2\u20ac\u0153living dead\u00e2\u20ac\u009d-the Big data.<\/p>\n

Googles\u00c2\u00a0 \u00e2\u20ac\u0153Big Query\u00e2\u20ac\u009d seems to be interesting. \u00c2\u00a0Availability of this to the public is even more interesting.<\/p>\n

Amazon elastic MapReduce is another effective way to extract information from massive volumes of data. How much this can scale is yet to be seen.<\/p>\n

Similarly , IBM\u00e2\u20ac\u2122s big data appliance is provided to retailers with Netezza; which is the IBM Netezza Customer Intelligence Appliance. The new Netezza appliance also seem to incorporate business-intelligence software. This could be a novel thinking. Retrieving and making sense of it.<\/p>\n

Within all these , Oracle has brought a set of services which looks like the usual standardized Oralces way of rolling out things. Out Of The Box solution. A set of tools and services has been rolled out. How far this can be taken to the field depends mainly on the people who are involved. \u00c2\u00a0But here is a look at all their tools and services and a brief introduction; all in one page.<\/p>\n

Oralce looks at Big Data problem in three main areas, as it should be. \u00c2\u00a0Within Big data problem, you inspect and retrieve, analyze and then present. So does Oracle;<\/p>\n

Oralce Acquire Big data says, will acquire from Oracle NoSQL database and Oracle Database 11g.<\/p>\n

The Organizing part of it consists of, Oracle Big Data appliance, Connectors and the oracle data integrator<\/p>\n

The Analyze components include, Oracle advanced analytics, Oracle data warehousing, Exadata and Exalytics In-Memory Machine ? I mean \u00e2\u20ac\u0153Machine\u00e2\u20ac\u009d J<\/span> interesting anyways.<\/p>\n

The Appliance:<\/u> <\/strong>seems to be integrated with cloudera, Hadoop and JVM in a linux composite.<\/p>\n

The Data Integrator: <\/strong>SOA modules and ETL<\/p>\n

The Connectors: <\/strong>Include the HDFS connectors and Hadoop loaders including something for data integrator application adapter for Hadoop. (Says it reduces MapReduce development efforts.) and OLTP.<\/p>\n

Below are other common Oracle components that now is extended for Big Data<\/strong><\/p>\n