Suppose you're given a dataset with 100 million rows and you need to perform a complex join operation between two tables. How would you optimize the join operation for better performance?

First, analyze the data distribution: if one table is much smaller, use a broadcast join to avoid shuffling. If both are large, partition both tables on the join key and use a hash join; ensure the hash table fits in memory by tuning the number of partitions. Use columnar storage (Parquet/ORC) to reduce I/O and enable predicate pushdown. Create indexes or materialized views on the join columns if the database supports it. Enable cost‑based optimizer hints (e.g., join order, join type) and use statistics to let the engine pick the best plan. For skewed keys, apply skew handling techniques such as salting or using a custom partitioner. Finally, parallelize the operation by increasing the number of worker nodes or using distributed SQL engines like Spark SQL or Presto, which can execute the join in a distributed fashion. These steps reduce shuffle size, memory usage, and overall execution time.