What are the different ways available in PySpark on Databricks to handle large data ingestion scenarios, and is there a particular approach that is recommended?

On Databricks, you can handle large ingestion by using Delta Lake for ACID transactions and schema enforcement, which automatically partitions data and optimizes reads. You can also use Spark’s built‑in partitioning and bucketing to distribute data across executors. For very large files, consider using Spark’s native support for reading compressed formats (Parquet, ORC) and enabling column pruning. If you need to join large datasets, use broadcast joins for small tables or repartition the larger table on the join key to reduce shuffle. Additionally, you can set spark.sql.autoBroadcastJoinThreshold to control when Spark automatically broadcasts. A recommended approach is to ingest data into Delta Lake, partition by a high‑cardinality column, and use broadcast joins for lookup tables that are small.