one more Acrolinx fix

Author: Kat-Campise

articles/synapse-analytics/synapse-link/how-to-query-analytical-store-spark.md

@@ -44,7 +44,7 @@ val df_olap = spark.read.format("cosmos.olap").
 
 ## Create Spark table
 
-In this gesture, you'll create a Spark table pointing to the container you selected. That operation doesn't incur any data movement. If you decide to delete that table, the underlying container (and corresponding analytical store) won't be impacted. 
+In this gesture, you'll create a Spark table pointing to the container you selected. That operation doesn't incur any data movement. If you decide to delete that table, the underlying container (and corresponding analytical store) won't be affected. 
 
 This scenario is convenient to reuse tables through third-party tools and provide accessibility to the data for the run-time.
 
@@ -60,7 +60,7 @@ create table call_center using cosmos.olap options (
 
 ## Write DataFrame to container
 
-In this gesture, you will write a dataframe into a container. This operation will impact the transactional performance and consume Request Units. Using Azure Cosmos DB transactional performance is ideal for write transactions. Make sure that you replace **YOURDATAFRAME** by the dataframe that you want to write back to.
+In this gesture, you'll write a dataframe into a container. This operation will impact the transactional performance and consume Request Units. Using Azure Cosmos DB transactional performance is ideal for write transactions. Make sure that you replace **YOURDATAFRAME** by the dataframe that you want to write back to.
 
 ```python
 # Write a Spark DataFrame into a Cosmos DB container
@@ -90,7 +90,7 @@ df.write.format("cosmos.oltp").
 ```
 
 ## Load streaming DataFrame from container
-In this gesture, you'll use Spark Streaming capability to load data from a container into a dataframe. The data will be stored into the primary data lake account (and file system) that you connected to the workspace. 
+In this gesture, you'll use Spark Streaming capability to load data from a container into a dataframe. The data will be stored in the primary data lake account (and file system) that you connected to the workspace. 
 
 If the folder */localReadCheckpointFolder* isn't created, it will be automatically created. This operation will impact the transactional performance of Cosmos DB.