What options are available in PySpark on Databricks to handle scenarios involving large datasets and joins?

Options include: 1) Broadcast joins using broadcast() or hint("broadcast") for small tables; 2) Shuffle joins with proper partitioning on the join key to minimize data movement; 3) Using DataFrame API with .join(..., "inner") and specifying join hints; 4) Leveraging Delta Lake’s Z‑Ordering and partition pruning; 5) Caching intermediate results with cache() or persist() to avoid recomputation; 6) Using map‑side joins via broadcast variables for lookup tables; 7) Adjusting spark.sql.autoBroadcastJoinThreshold to control broadcast behavior; 8) Using repartition or coalesce to balance data across executors. Choosing the right combination depends on data size, memory constraints, and the join cardinality.