Improving Big Data Visualization and Association Rule Mining through Splunk Database Gap Analysis

Managing and extracting insights from large-scale machine data is challenging.Splunk, a powerful platform for indexing and searching log files, has notable gaps in its database, limiting its extensive data handling capabilities.This paper presents a methodology to enhance Splunk's performance, including mitigating database gaps, optimizing visualizations, and implementing association rule mining directly in Splunk.Unlike cloud-native solutions like Sumo Logic, Splunk's user experience and search performance are less effective with big datasets.We explore advanced visualization techniques to handle large data volumes, as traditional tools like Tableau struggle, requiring custom Python-based visualizations.Additionally, we identify a need for robust association rule mining in Splunk.Our findings offer a roadmap for maximizing machine data potential through advanced analytics and insights.