(AzureCXP) MicrosoftDocs/azure-docs#75234

Fixing all code snippets after "Define the input source" text are marked incorrectly with SQL instead of Scala.

Author: CHEEKATLAPRADEEP-MSFT

articles/hdinsight/spark/apache-spark-structured-streaming-overview.md

@@ -68,7 +68,7 @@ These JSON files are stored in the `temps` subfolder underneath  the HDInsight c
 
 First configure a DataFrame that describes the source of the data and any settings required by that source. This example draws from the JSON files in Azure Storage and applies a schema to them at read time.
 
-```sql
+```scala
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.functions._
 
@@ -86,23 +86,23 @@ val streamingInputDF = spark.readStream.schema(jsonSchema).json(inputPath)
 
 Next,  apply a query that contains the desired operations against the Streaming DataFrame. In this case, an aggregation groups all the rows into 1-hour windows, and then computes the minimum, average, and maximum temperatures in that 1-hour window.
 
-```sql
+```scala
 val streamingAggDF = streamingInputDF.groupBy(window($"time", "1 hour")).agg(min($"temp"), avg($"temp"), max($"temp"))
 ```
 
 ### Define the output sink
 
 Next,  define the destination for the rows that are added to the results table within each trigger interval. This example  just outputs all  rows to an in-memory table `temps` that you can later query with SparkSQL. Complete  output mode ensures that all rows for all windows are output every time.
 
-```sql
+```scala
 val streamingOutDF = streamingAggDF.writeStream.format("memory").queryName("temps").outputMode("complete")
 ``` 
 
 ### Start the query
 
 Start the streaming query and run until a termination signal is received.
 
-```sql
+```scala
 val query = streamingOutDF.start() 
 ```