You have an Azure subscription that contains an Azure Data Lake Storage Gen2 container named Container1 and an Azure Synapse Analytics workspace named Workspace1. Workspace1 contains multiple Apache S

Disabling hierarchical namespaces on an ADLS Gen2 container removes directory-level operations and would degrade, not improve, performance for workloads that rely on directory semantics.

Spark's DataFrame.cache() (or persist()) stores the dataset in executor memory after the first read, so subsequent jobs or actions access in-memory data instead of re-reading from ADLS Gen2, directly reducing the I/O bottleneck that slows repeated reads of a large shared dataset.

Increasing spark.sql.autoBroadcastJoinThreshold allows larger tables to be broadcast in join operations, which optimizes join performance but does not address repeated full reads of a large shared dataset.

Using RDDs instead of DataFrames bypasses Spark's Catalyst optimizer and Tungsten execution engine, which typically results in worse, not better, performance for large datasets.