How do you optimize a data model to improve query performance, for example, reducing a query that currently takes minutes to run?

Optimizing a data model involves both design choices and query‑level tweaks. First, analyze the slow query with an execution plan to identify bottlenecks: full table scans, large joins, or missing indexes. Then:

1. Schema design: Use dimensional modeling (star/snowflake) to separate facts and dimensions. Keep fact tables denormalized and partitioned by a high‑cardinality column (e.g., date). Dimension tables should be small and indexed on surrogate keys.

1. Indexing: Create composite indexes on columns used in joins and filters. In columnar engines (e.g., Snowflake, Redshift), use sort keys or distribution keys to co‑locate related rows.

1. Materialized views / aggregates: Pre‑compute expensive aggregations or joins into materialized views or summary tables. Refresh them on a schedule or incrementally.

1. Partitioning & clustering: Partition large tables by date or region to prune data early. In Databricks Delta, use Z‑order clustering on frequently queried columns.

1. Query rewriting: Replace correlated subqueries with joins, use window functions sparingly, and avoid SELECT *; only fetch needed columns.

1. Caching: In interactive environments, cache frequently accessed tables or query results.

1. Resource scaling: Ensure the compute cluster has enough workers and memory; auto‑scale if the workload spikes.

By iterating through these steps—profiling, redesigning the schema, indexing, materializing, and tuning queries—you can often reduce a multi‑minute query to seconds, improving overall system performance and user experience.