[SPARK-46357] Replace incorrect documentation use of setConf with conf.set

docs/sql-data-sources-avro.md

@@ -330,7 +330,7 @@ Data source options of Avro can be set via:
   </tr></table>
 
 ## Configuration
-Configuration of Avro can be done using the `setConf` method on SparkSession or by running `SET key=value` commands using SQL.
+Configuration of Avro can be done via `spark.conf.set` or by running `SET key=value` commands using SQL.
 <table>
   <thead><tr><th><b>Property Name</b></th><th><b>Default</b></th><th><b>Meaning</b></th><th><b>Since Version</b></th></tr></thead>
   <tr>

docs/sql-data-sources-parquet.md

@@ -431,7 +431,7 @@ Other generic options can be found in <a href="https://spark.apache.org/docs/lat
 
 ### Configuration
 
-Configuration of Parquet can be done using the `setConf` method on `SparkSession` or by running
+Configuration of Parquet can be done via `spark.conf.set` or by running
 `SET key=value` commands using SQL.
 
 <table>

docs/sql-performance-tuning.md

@@ -31,7 +31,7 @@ Spark SQL can cache tables using an in-memory columnar format by calling `spark.
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
 memory usage and GC pressure. You can call `spark.catalog.uncacheTable("tableName")` or `dataFrame.unpersist()` to remove the table from memory.
 
-Configuration of in-memory caching can be done using the `setConf` method on `SparkSession` or by running
+Configuration of in-memory caching can be done via `spark.conf.set` or by running
 `SET key=value` commands using SQL.
 
 <table>
@@ -297,7 +297,7 @@ This feature coalesces the post shuffle partitions based on the map output stati
    </tr>
  </table>
 
-### Spliting skewed shuffle partitions
+### Splitting skewed shuffle partitions
  <table>
    <thead><tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since Version</th></tr></thead>
    <tr>