If we have a huge table in Amazon Redshift, what options would you consider to optimize its performance?

For a large Redshift table, start by choosing the right distribution style: KEY distributes rows based on a column that is frequently joined, ALL duplicates the table on every node (good for small dimension tables), and EVEN balances rows evenly. Use a sort key on columns that are used in range predicates or joins to enable pruning. Apply columnar compression encodings (e.g., LZO, ZSTD) to reduce storage and I/O. Run VACUUM regularly to reclaim space and maintain sort order. If the table is read‑heavy, consider creating a materialized view or a summary table. For very large datasets, use Redshift Spectrum to query data stored in S3 without loading it into the cluster. Enable concurrency scaling if you expect spikes in query load. Finally, monitor query performance with EXPLAIN and adjust statistics with ANALYZE to ensure the planner has accurate estimates.