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Cassandra and SQL database comparison for near real-time Twitter data warehouse

机译：Cassandra和SQL数据库比较，用于近实时Twitter数据仓库

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In the era of Big Data, social media analysis has grown extremely popular. Twitter, one of the most popular social media, is believed to contain many user opinion in its message. Thus, leading organizations start utilizing its data using social media analytic tools to get in-sight of their markets in real-time. However, many Twitter analytic tools are still specified only in some specific tasks. Therefore, in order to enhance the possibility of doing many analysis on Twitter, a data warehouse technology can be utilized to receive, process, and store a real-time Twitter streams. Nonetheless, data warehouse development using relational database start to show its limit on storing big data let alone real-time. Hence, NoSQL (Not Only SQL) technology has emerged as an alternative solution. In this paper, we try to develop near real-time Twitter data warehouse using NoSQL database, Cassandra, and compare its storing and querying performance with that developed using relational databases. The results show that Cassandra performs significantly better in storing data than the relational databases. Meanwhile, in its querying performance, Cassandra is slower while using small data but way faster on vast data.
机译：在大数据时代，社交媒体分析已变得非常流行。 Twitter是最受欢迎的社交媒体之一，据信在其消息中包含许多用户意见。因此，领先的组织开始使用社交媒体分析工具利用其数据来实时了解其市场。但是，许多Twitter分析工具仍仅在某些特定任务中指定。因此，为了增加在Twitter上进行许多分析的可能性，可以利用数据仓库技术来接收，处理和存储实时Twitter流。尽管如此，使用关系数据库进行数据仓库开发开始显示出其在存储大数据方面的局限性，更不用说实时了。因此，NoSQL（不仅是SQL）技术已经成为一种替代解决方案。在本文中，我们尝试使用NoSQL数据库Cassandra开发近乎实时的Twitter数据仓库，并将其存储和查询性能与使用关系数据库开发的存储和查询性能进行比较。结果表明，Cassandra在存储数据方面的性能明显优于关系数据库。同时，在查询性能方面，Cassandra在使用小数据时速度较慢，但​​在海量数据上速度更快。

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