Lessons Learned Running Presto at Meta Scale

1. 1Identify potential bottlenecks in Presto deployment
2. 2Implement scalable architecture to handle growing demands
3. 3Monitor and optimize query performance
4. 4Develop strategies for troubleshooting and debugging

Data engineers and architects on a team can benefit from understanding the challenges and solutions for scaling Presto, as it can inform their own decisions for large-scale data querying

💡 Scaling Presto requires careful planning, monitoring, and optimization to overcome unexpected challenges

🚀 Scaling Presto to Meta scale: lessons learned from 10 years of deployment