Abstract
Everybody is connected with social media like (Facebook, Twitter, LinkedIn, Instagram…etc.) that generate a large quantity of data and which traditional applications are inadequate to process. Social media are regarded as an important platform for sharing information, opinion, and knowledge of many subscribers. These basic media attribute Big data also to many issues, such as data collection, storage, moving, updating, reviewing, posting, scanning, visualization, Data protection, etc. To deal with all these problems, this is a need for an adequate system that not just prepares the details, but also provides meaningful analysis to take advantage of the difficult situations, relevant to business, proper decision, Health, social media, science, telecommunications, the environment, etc. Authors notice through reading of previous studies that there are different analyzes through HADOOP and its various tools such as the sentiment in real-time and others. However, dealing with this Big data is a challenging task. Therefore, such type of analysis is more efficiently possible only through the Hadoop Ecosystem. The purpose of this paper is to analyze literature related analysis of big data of social media using the Hadoop framework for knowing almost analysis tools existing in the world under the Hadoop umbrella and its orientations in addition to difficulties and modern methods of them to overcome challenges of big data in offline and real –time processing. Real-time Analytics accelerates decision-making along with providing access to business metrics and reporting. Comparison between Hadoop and spark has been also illustrated.
Highlights
There are more than one billion social media network users, many of whom are regularly involved around the world and can be Linked through their phones and tablets
Machines can learn how to extract group messages from public Facebook pages automatically, use API graph calls, filter out messages without opinion, classify their feelings about interest patterns and the aim of this model is to create a big data application that gets a stream of public data from the social media network of Facebook, which can help law enforcement and cybercrime analysts evaluate and monitor social media in the search for digital monitoring of violence or extremism, which can be used in more digital forensic investigations
Conclusion and Future work: Hadoop is commonly used with map reduce technique for the processing of big data
Summary
There are more than one billion social media network users, many of whom are regularly involved around the world and can be Linked through their phones and tablets. Which are primarily collected from social networks by Apache Flume unstructured format and the JAQL script used to extract important data, converting them into a simpler structure to convert the delimited file by commas, and stored in Hadoop storage to perform the processing using Map Reduce. Machines can learn how to extract group messages from public Facebook pages automatically, use API graph calls, filter out messages without opinion, classify their feelings about interest patterns (i.e. positive and negative) and the aim of this model is to create a big data application that gets a stream of public data from the social media network of Facebook, which can help law enforcement and cybercrime analysts evaluate and monitor social media in the search for digital monitoring of violence or extremism, which can be used in more digital forensic investigations In this context, Hadoop with Apache Hive, Apache HBase. View job scheduling algorithms that analyze their characteristics, strength and weakness in each system
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