The article explores the implementation of a hot-warm-cold architecture using Elasticsearch's Index Lifecycle Management (ILM), a feature designed to efficiently manage data indexes. This architecture is particularly suited for time series data like logs and metrics, where data is categorized into hot, warm, and cold phases based on its usage and importance. The hot phase contains the most accessed data, requiring high CPU and fast I/O, while the warm and cold phases store less frequently accessed data, needing more disk space but less processing power. ILM allows users to define and automate the movement of data between these phases, optimizing costs and performance. It includes actions like rollover, force merge, and freeze to manage data efficiently, with policies tailored to specific needs. The article also highlights configuring ILM policies for Beats and Logstash, demonstrating how ILM simplifies data management without needing external tools like Curator. With Elasticsearch version 7.0, ILM is enabled by default for Beats and Logstash, further streamlining the process.