Coralogix 2.0 - Machine learning powered log analytics, made for startups

@otnilevi Awesome question. Most companies index and visualize your data and allow you to create user defined alerts and that’s great. But even small and medium companies today generate tens of millions of logs a day, and having a centralized place to search, slice and dice these logs is sometimes not enough. We automatically understand the log data structure in order to turn millions of log records back into their unique patterns, then find connections between those patterns to form the baseline flows of your software and alert you once they break. That way we solve the two main problems companies have with their logs today: 1) There are too many logs to handle 2) Companies are reactive instead of proactive.

Thank you @orgarfunkel !

@levy_josef thank you so much ! You know You are one of our most veteran customer ? from the Beta and now 2.0.

Hi @danbrahmy, thanks for your interest :) We implement many kinds of algorithms at Coralogix; these include Bloom Filters and HyperLogLog for membership heuristics, agglomerative tree analysis, multivariate distribution MLE/MAP, multiple time-series correlations, regressions, and more. Chaining these algorithms together enables us to reverse-engineer the types of log-records you have, construct the patterns and flows of your system and alert on anomalies. p.s. You may read about the HyperLogLog algorithm we're using and other technical details on our company blog: https://coralogix.com/process-bi...