The log analytics industry is becoming increasingly complex as organizations generate large volumes of log data from their cloud-based applications and infrastructure services. The process of transforming this raw data into a structured format for analysis, known as data transformation, can add significant costs and complexity to the log analytics process. Data transformation involves converting data from its "raw" source format into a "structured" destination format that's ready for analysis, which requires manual processes such as data discovery, mapping, code generation, and review. The ELK stack, a popular log analytics solution, can also lead to increased costs and complexity due to the growing cost of inputting and outputting data, re-indexing, and computing resources required for large-scale data transformation. However, ChaosSearch is revolutionizing data transformation in the cloud by eliminating the need for manual processes, reducing costs, and simplifying the log analytics process through its powerful new methodology called the Chaos Data Refinery.