Fixed typos in docs

docs/sql-programming-guide.md

@@ -571,7 +571,7 @@ be created by calling the `table` method on a `SparkSession` with the name of th
 For file-based data source, e.g. text, parquet, json, etc. you can specify a custom table path via the
 `path` option, e.g. `df.write.option("path", "/some/path").saveAsTable("t")`. When the table is dropped,
 the custom table path will not be removed and the table data is still there. If no custom table path is
-specifed, Spark will write data to a default table path under the warehouse directory. When the table is
+specified, Spark will write data to a default table path under the warehouse directory. When the table is
 dropped, the default table path will be removed too.
 
 Starting from Spark 2.1, persistent datasource tables have per-partition metadata stored in the Hive metastore. This brings several benefits:

docs/structured-streaming-programming-guide.md

@@ -778,7 +778,7 @@ windowedCounts = words \
 In this example, we are defining the watermark of the query on the value of the column "timestamp", 
 and also defining "10 minutes" as the threshold of how late is the data allowed to be. If this query 
 is run in Update output mode (discussed later in [Output Modes](#output-modes) section), 
-the engine will keep updating counts of a window in the Resule Table until the window is older 
+the engine will keep updating counts of a window in the Result Table until the window is older
 than the watermark, which lags behind the current event time in column "timestamp" by 10 minutes.
 Here is an illustration.