tldr - powered by Generative AI

• Kubernetes is used for running stateful sets and taking advantage of its features for data management, database management, application monitoring, application deployment monitoring, logging, machine learning, and university management
• The presentation discusses the use of Apache Druid for data ingestion and reconciliation
• The presentation highlights the importance of RAM for Druid clusters and the benefits of caching segments
• The presentation discusses the plan to migrate to version 325 and Java 17, decrease costs by migrating to ARM, and use the TCD and Kubernetes API for endpoint and information retrieval
• The presentation acknowledges the challenges of running stateful sets on Kubernetes but believes it is the best option
• The presentation expresses gratitude to the Druid and Druid operational community for their support

tldr - powered by Generative AI

• Use metrics from inside Elasticsearch for accuracy
• Scale in larger increments to reduce noise
• Force index rotation to evenly spread load across nodes
• Judge cluster size based on disk usage and search latency
• Use local SSDs for better I/O latency
• Consider hot-warm-cold architecture for data management