[DOCS][MINOR] Fixed a few typos in the Structured Streaming documentation

docs/structured-streaming-programming-guide.md

@@ -713,15 +713,15 @@ old windows correctly, as illustrated below.
 
 ![Handling Late Data](img/structured-streaming-late-data.png)
 
-However, to run this query for days, it's necessary for the system to bound the amount of 
+However, to run this query for days, it is necessary for the system to bound the amount of 
 intermediate in-memory state it accumulates. This means the system needs to know when an old 
 aggregate can be dropped from the in-memory state because the application is not going to receive 
 late data for that aggregate any more. To enable this, in Spark 2.1, we have introduced 
-**watermarking**, which let's the engine automatically track the current event time in the data and
+**watermarking**, which lets the engine automatically track the current event time in the data
 and attempt to clean up old state accordingly. You can define the watermark of a query by 
-specifying the event time column and the threshold on how late the data is expected be in terms of 
+specifying the event time column and the threshold on how late the data is expected to be in terms of 
 event time. For a specific window starting at time `T`, the engine will maintain state and allow late
-data to be update the state until `(max event time seen by the engine - late threshold > T)`. 
+data to update the state until `(max event time seen by the engine - late threshold > T)`. 
 In other words, late data within the threshold will be aggregated, 
 but data later than the threshold will be dropped. Let's understand this with an example. We can 
 easily define watermarking on the previous example using `withWatermark()` as shown below.
@@ -792,7 +792,7 @@ This watermark lets the engine maintain intermediate state for additional 10 min
 data to be counted. For example, the data `(12:09, cat)` is out of order and late, and it falls in
 windows `12:05 - 12:15` and `12:10 - 12:20`. Since, it is still ahead of the watermark `12:04` in 
 the trigger, the engine still maintains the intermediate counts as state and correctly updates the 
-counts of the related windows. However, when the watermark is updated to 12:11, the intermediate 
+counts of the related windows. However, when the watermark is updated to `12:11`, the intermediate 
 state for window `(12:00 - 12:10)` is cleared, and all subsequent data (e.g. `(12:04, donkey)`) 
 is considered "too late" and therefore ignored. Note that after every trigger, 
 the updated counts (i.e. purple rows) are written to sink as the trigger output, as dictated by 
@@ -825,7 +825,7 @@ section for detailed explanation of the semantics of each output mode.
 same column as the timestamp column used in the aggregate. For example, 
 `df.withWatermark("time", "1 min").groupBy("time2").count()` is invalid 
 in Append output mode, as watermark is defined on a different column
-as the aggregation column.
+from the aggregation column.
 
 - `withWatermark` must be called before the aggregation for the watermark details to be used. 
 For example, `df.groupBy("time").count().withWatermark("time", "1 min")` is invalid in Append 
@@ -909,7 +909,7 @@ track of all the data received in the stream. This is therefore fundamentally ha
 efficiently.
 
 ## Starting Streaming Queries
-Once you have defined the final result DataFrame/Dataset, all that is left is for you start the streaming computation. To do that, you have to use the `DataStreamWriter`
+Once you have defined the final result DataFrame/Dataset, all that is left is for you to start the streaming computation. To do that, you have to use the `DataStreamWriter`
 ([Scala](api/scala/index.html#org.apache.spark.sql.streaming.DataStreamWriter)/[Java](api/java/org/apache/spark/sql/streaming/DataStreamWriter.html)/[Python](api/python/pyspark.sql.html#pyspark.sql.streaming.DataStreamWriter) docs)
 returned through `Dataset.writeStream()`. You will have to specify one or more of the following in this interface.
 
@@ -930,7 +930,7 @@ There are a few types of output modes.
 new rows added to the Result Table since the last trigger will be 
 outputted to the sink. This is supported for only those queries where 
 rows added to the Result Table is never going to change. Hence, this mode 
-guarantees that each row will be output only once (assuming 
+guarantees that each row will be outputted only once (assuming 
 fault-tolerant sink). For example, queries with only `select`, 
 `where`, `map`, `flatMap`, `filter`, `join`, etc. will support Append mode.
 
@@ -1396,15 +1396,15 @@ You can directly get the current status and metrics of an active query using
 `lastProgress()` returns a `StreamingQueryProgress` object 
 in [Scala](api/scala/index.html#org.apache.spark.sql.streaming.StreamingQueryProgress) 
 and [Java](api/java/org/apache/spark/sql/streaming/StreamingQueryProgress.html)
-and an dictionary with the same fields in Python. It has all the information about
+and a dictionary with the same fields in Python. It has all the information about
 the progress made in the last trigger of the stream - what data was processed, 
 what were the processing rates, latencies, etc. There is also 
 `streamingQuery.recentProgress` which returns an array of last few progresses.  
 
-In addition, `streamingQuery.status()` returns `StreamingQueryStatus` object 
+In addition, `streamingQuery.status()` returns a `StreamingQueryStatus` object 
 in [Scala](api/scala/index.html#org.apache.spark.sql.streaming.StreamingQueryStatus) 
 and [Java](api/java/org/apache/spark/sql/streaming/StreamingQueryStatus.html)
-and an dictionary with the same fields in Python. It gives information about
+and a dictionary with the same fields in Python. It gives information about
 what the query is immediately doing - is a trigger active, is data being processed, etc.
 
 Here are a few examples.