Data tiering in Imply and Apache Druid is a strategy used to optimize database performance by organizing data based on its value, usage frequency, and the urgency of queries. "Hot" tiers handle recent, frequently accessed data that require fast query responses, while "cold" tiers store older, less frequently accessed data suited for long-running queries and strategic analysis. Imply leverages Apache Druid's architecture, which involves dividing data into segments across various nodes, to implement tiering strategies based on either storage or compute needs. Storage tiering organizes data by the time it was generated or by the frequency of queries, using different hardware configurations to optimize performance. Compute tiering, on the other hand, is used for environments with multitenant clusters and mixed workloads, allowing data separation by operational requirements such as CPU or RAM. These tiering strategies ensure efficient data management and query performance, tailored to specific operational needs.