I have a very large 20 GB file that I receive from a vendor. I want to process this file using Spark. How would you decide how to split this file into multiple chunks, and how would you set the partitions based on the data? What strategy would you use?

In Spark, the most common way to split a large file is to rely on the underlying file system’s block size (e.g., HDFS block size of 128 MB). Spark will automatically create one partition per block, which gives you parallelism without manual chunking. If you need finer control, you can use repartition() or coalesce() after loading the data to adjust the number of partitions to match the number of executors or cores. A good rule of thumb is to have 2–4 partitions per executor core to keep all cores busy while avoiding excessive shuffle. When the data is structured (e.g., CSV, Parquet), you can also use partition columns (date, region) to create logical partitions that align with query patterns. Finally, consider the size of each partition: aim for 100–200 MB per partition to balance I/O and memory usage. The overall strategy is: let the file system handle the initial split, then adjust partitions in Spark to match cluster resources and workload characteristics.