Social data analysis has become a vital tool for businesses and organisations for mining data from social media and analysing for diverse purposes such as customer opinion mining, pattern recognition and predictive analytics. However, the high level of volatility for social data means application updates due to analytical results requires spontaneous integration. In addition, while cloud computing has been hugely utilised to address computational overhead issues due to the volume of social data, results obtained still fall short of expected levels. Hence, a persistence mechanism for rapid deployment and integration of software updates for the analytical process is proposed. The persistence mechanism constitutes a significant component within a novel methodology which also leverages cloud computing, microservices and orchestration for online social data analysis, one which fully maximises cloud capabilities and fosters optimisation of cloud computing resources. The proposed methodology provides means of delivering real-time, persistent social data analytics as a cloud service, thereby facilitating spontaneous integration of solutions to maximise expectations from targeted social media audience.

社交数据分析已成为企业和组织从社交媒体中挖掘数据并进行各种分析（例如客户意见挖掘，模式识别和预测分析）的重要工具。但是，社交数据的高波动性意味着由于分析结果而导致的应用程序更新需要自发集成。此外，尽管由于社交数据量大，云计算已被广泛用于解决计算开销问题，但所获得的结果仍未达到预期水平。因此，提出了一种用于分析过程的软件更新的快速部署和集成的持久性机制。持久性机制构成了一种新颖的方法学的重要组成部分，该方法学还利用了云计算，微服务和业务流程进行在线社交数据分析，这是一种充分利用云功能并促进云计算资源优化的方法。所提出的方法论提供了将实时，持续的社交数据分析作为云服务交付的方法，从而促进了解决方案的自发集成，从而最大程度地提高了目标社交媒体受众的期望。