Finance Case: Online Log Analysis

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Hi, everyone! Today I’m going to introduce you fusioninsight Online Log Analysis solution in finance. 

analysis log


Online   Log Analysis

Customer pain points

1.Most banking systems only handle alarms in   real time and report the monitoring information but cannot monitor KPIs and   process run logs in real time.   

2.Limited by disk space, the banking system can   store O&M data only for several months. Expired data will be deleted.   This disables the risk analysis and fault prediction functions that rely on   historical data.  


1.Real-time collection and analysis on   existing application system logs are performed to provide second-level   monitoring capabilities for top applications, involving transaction   information and resource consumption information.   

2.For faults and abnormalities, real-time   log search capabilities are supported.  

3.For historical logs, fault prediction   capabilities are supported by data mining and analysis.  


Characteristics:   The stream framework is employed to collect logs in real time, as well as   analyze and display logs. This framework will be used in data prediction.

Solution implementation: Flume (real-time collection) + Kafka   (message queue) + Streaming (stream processing)

Banks have access to enormous amounts of data about their customers, but due to multiple constraints this data is not yet sufficiently converted into useful insights.

With competition in the financial services sector getting fiercer, banks need to adopt a data-driven approach if they want to stay competitive. As opportunities for incumbent banks and insurers from these insights are almost unlimited, Big Data will be a strong differentiator in the future competitiveness of financial institutions.

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