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NK Transforms Big Data into Actionable Insights that Drive Business Success
技术
- 分析与建模 - 大数据分析
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- 电信
适用功能
- 销售与市场营销
- 商业运营
用例
- 实时定位系统 (RTLS)
- 质量预测分析
服务
- 数据科学服务
挑战
NK.pl, a popular social networking service in Poland, was facing a challenge in keeping pace with the changing preferences and behaviors of its 13 million users. The traditional transaction database used by NK.pl was not powerful enough to process the 250 terabytes of data generated by its users in a timely manner. When product managers attempted to run queries against the data, which was spread across 1,500 servers, they faced query response times of three to four weeks. Such slow response times were unacceptable to NK.pl because the data was outdated and meaningless by the time it was received. To optimize product features, marketing efforts, and the experience of its social network members, NK.pl needed a system that could perform both simple and rich queries quickly for intra-day analysis and response in an extremely cost-effective manner.
关于客户
NK (NK.pl) is a Polish social networking service that brings students and alumni together online. Created in 2006 by 24-year-old Maciej Popowicz, a computing student in Poland’s high-tech hub of Wroclaw, NK is an abbreviation of “Nasza Klasa” which literally means “our class”. The site has been compared to Britain’s Friends Reunited, France’s Copains d’avant, and the US’s Classmates, and Facebook. Yet NK.pl is more popular than Facebook in Poland, boasting more than 13 million active users compared to Facebook’s 6.5 million (in Poland). According to Google’s annual Zeitgeist list of commonly used Web search terms, NK was the seventh-fastest rising term in the world in 2008. The success of NK.pl has been attributed to its high-quality social networking features, online gaming offerings, and targeted advertising capabilities.
解决方案
In an effort to speed query response times, NK.pl built a data warehouse that would open new windows into data stored in the transaction database. Split into two main parts, the main “fast” analytics portion now sits on a single server and is powered by the VectorWise analytic database. The other main part contains large data storage and analytics powered by Hadoop solutions. Hadoop technology stores click-stream data, such as POST and GET requests as well as AdServer logs. Before integrating Vectorwise into the data analysis infrastructure, the data warehouse could not deliver the query response times that the organization required. Now, employees use Vectorwise to achieve fast, actionable insights to make more fully informed strategic business decisions.
运营影响
数量效益
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