Digging into Social Networking Big Data Challenges, Opportunities, and Solutions
Big Data in Social Networking is gaining popularity nowadays with every new user joining world leading networks. Social networking paradigm is constantly shifting and the networks themselves are gaining more and more functionality into their structure.
The increasing amount of analytics brings to life novel approaches to the existing schemes of Big Data usage. That’s why social networking analytics is of utter importance nowadays and the data sets vary due to different types of modern social networks.
The rapid growth of Big Data international development is revolutionizing global business and our daily lives as well. Nowadays, we are witnessing the growing impact of such spheres like e-commerce and Advertising on the social networking world. At the same time, there are two main aspects of Big Data in Social Media that are worth paying attention to.
Big Data and Social Media
1) One aspect is connected with the way how the analysis is conducted based on Big Data.
2) The other aspect deals with the usage of Big Data analytics to secure social network from malicious attackers.
The current post tends to highlight some issues of the first approach and discuss one of the latest projects of Big Data implementation into the globally known social network conducted by our team.
Scand Solution for Analysis of Social Networking Advertising Campaigns with the help of Big Data Technology
The customer hired our team to provide the analysis of users’ digital profiles: online behavioral patterns, viral patterns detection, collaborative filtering and many other. Such a detailed analysis was needed to target the ad campaigns online successfully. The solution required a flexible access provision to all needed analytics: dynamic and static digital profiles, demographic information, etc.
The Big Data solution developed by us is capable of supporting the following features:
• Procession of huge amounts of data;
• Generation of automatic reports;
• Visual analytics (charts and tables);
• Summary views;
• Analytics in the form of a dashboard;
• Usage of Kafka and Hbase to gather analytics to key value storage;
• Data capture with the help of Cassandra;
• Procession of SQL alike requests through Hive;
• Targeted campaigns.
The expertise our team has in Big Data development helped to complete the project and hit all its goals. It facilitated the advertising campaign navigation within the social network and let the campaigns run smoothly and tackle the most targeted audience.