Cyber threats are becoming more sophisticated, and enterprise security teams are under constant pressure to improve and enhance their threat detection and response capabilities. But as security teams expand their security logging tools and capabilities, the burden of monitoring those tools and investigating alerts grows exponentially.

ChaosSearch was built for live analytics at scale on cloud storage. Our architecture was designed for high volume ingestion of streams & analytics at scale via ElasticSearch & Trino API via a stateless fabric that can scale to meet the customers’ scale & latency requirements. Because we don’t store any data, under the hood, ChaosSearch is basically a set of containers that are deployed in cloud compute instances in a dedicated VPC to each customer managed by ChaosSearch.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.

The ephemeral nature of the cloud has made compliance and security a greater challenge for organizations. The volume of data that companies must collect and retain from their cloud services, depending on their industry, is ballooning fast. According to ESG, 71% of companies believe their observability data (logs, metrics and traces) is growing at a concerning rate. Even so, outcomes are getting worse, not better. Six out of 10 teams are unable to prevent issues before customers are impacted.