When writing data, are multiple partitions created?

When you write data to a distributed storage system like HDFS or a data lake, the system typically splits the data into multiple partitions or blocks to enable parallel processing and fault tolerance. In Spark, for example, each partition is a logical chunk of data that can be processed independently by a task. When you write a DataFrame to a file format such as Parquet, Spark will create one file per partition by default. The number of partitions is determined by the input data size, the number of cores, and any explicit partitioning you specify using .repartition() or .coalesce(). If you write to a partitioned table, Spark will also create subdirectories for each partition key. Therefore, yes, writing data often results in multiple partitions being created, unless you explicitly coalesce to a single partition.