Many teams consider Elasticsearch as a single platform to handle all their data needs, from search to analytics. However, this approach often brings hidden costs that are not immediately obvious. Performance can degrade as data volume grows, requiring careful index management and cluster tuning. Maintenance overhead increases significantly, as Elasticsearch clusters demand constant monitoring and optimization. Scaling horizontally adds complexity and can lead to higher infrastructure bills. Additionally, using Elasticsearch for workloads it wasn't designed for, such as complex aggregations or long-term storage, can result in suboptimal performance and increased resource consumption. This article discusses these trade-offs, helping architects make informed decisions about whether Elasticsearch should be their unified platform or just one component in a multi-system architecture.
Explore the trade-offs of using Elasticsearch as a unified data platform, including performance, maintenance, and scaling costs.