Fast dimensional analysis for root cause analysis at scale

Introduced a Fast Dimensional Analysis (FDA) framework to automate root cause analysis on structured logs at scale. The framework combines logs from multiple sources, deduplicates data at query time, one-hot encodes features, and applies frequent pattern mining (Apriori/FP-Growth) with support and lift metrics to identify correlated features that explain failure events. Pre- and post-processing, parallelism, and filters were implemented to further speed up analysis and improve interpretability.