Klout wanted to give consumers, brands, and partners faster, more detailed insight into hundreds of terabytes of social-network data. It also wanted to boost efficiency. To do so, Klout deployed a business intelligence solution based on Microsoft SQL Server 2012 Enterprise and Apache Hadoop. As a result, Klout processes data queries in near real time, minimizes costs, boosts efficiency, increases insight, and facilitates innovation.
With its new solution, Klout expects to boost efficiency, reduce expenses, expand insight, and support innovation.
Klout helps clients make sense of the hundreds of terabytes of date generated each day by more than 1 billion signals on 15 leading social networks including Facebook and LinkedIn. “We take in raw data and make it into something that is actionable for our consumers, brands, and partners,” says David Mariani, Vice President of Engineering at Klout.
The data that Klout analyzes is generated by the more than 100 million people who are indexed by the firm. This includes Klout members and the people that they interact with on social sites. Individuals join Klout to understand their influence on the web, which is rated on a scale from 1 to 100. They also sign up to participate in campaigns where they can receive gifts and free services. More than 3,500 data partners also join Klout to better understand consumers and network trends including changes in demand and how peoples’ influence might affect word-of-mouth advertising. To deliver the level of insight that customers seek and yet meet the budget constraints of a startup firm, Klout maintained a custom infrastructure based on the open-source Apache Hadoop framework, which provides distributed processing of large data sets. The solution included a separate silo for the data from each social network. To manage queries, Klout used custom web services, each with distinct business logic, to extract data from the silos and deliver it as a data mashup.