Explore strategies for efficiently managing and refining systems data in large-scale observability infrastructures without compromising data fidelity. Learn how to scale back the flood of data while retaining crucial information for troubleshooting and understanding production behaviors. Discover statistical techniques to gather accurate, specific, and error-bounded data on services' top-level performance and inner workings. Examine methods to keep context of anomalous data flows and cases while preventing ordinary data from overwhelming the system. Delve into three key strategies: reducing data volume, reusing information through sampling techniques, and recycling data through aggregation. Understand the importance of structuring data, implementing effective sampling rates, and utilizing aggregation as a last resort. Gain insights on normalizing per-key, retaining errors and slow queries, and harmonizing metrics with events to create a robust observability system that balances cost-effectiveness with comprehensive system insights. Read more