Merge pull request #214088 from SnehaGunda/SynapseBugs

Fixing grammar issues, Adding link to the query & tooling sections

Author: prmerger-automator[bot]

articles/synapse-analytics/get-started-analyze-spark.md

@@ -7,7 +7,7 @@ ms.reviewer: sngun
 ms.service: synapse-analytics
 ms.subservice: spark
 ms.topic: tutorial
-ms.date: 03/24/2021
+ms.date: 10/10/2022
 ---
 
 # Analyze with Apache Spark
@@ -25,7 +25,7 @@ In this tutorial, you'll learn the basic steps to load and analyze data with Apa
 
 ## Understanding serverless Apache Spark pools
 
-A serverless Spark pool is a way of indicating how a user wants to work with Spark. When you start using a pool, a Spark session is created if needed. The pool controls how many Spark resources will be used by that session and how long the session will last before it automatically pauses. You pay for spark resources used during that session not for the pool itself. In this way a Spark pool lets you work with Spark, without having to worry managing clusters. This is similar to how a serverless SQL pool works.
+A serverless Spark pool is a way of indicating how a user wants to work with Spark. When you start using a pool, a Spark session is created if needed. The pool controls how many Spark resources will be used by that session and how long the session will last before it automatically pauses. You pay for spark resources used during that session and not for the pool itself. This way a Spark pool lets you use Apache Spark without managing clusters. This is similar to how a serverless SQL pool works.
 
 ## Analyze NYC Taxi data with a Spark pool
 
@@ -74,7 +74,7 @@ Data is available via the dataframe named **df**. Load it into a Spark database
    ```
 
 1. Run the cell to show the NYC Taxi data we loaded into the **nyctaxi** Spark database.
-1. Create a new code cell and enter the following code. We will analyze this data and save the results into a table called **nyctaxi.passengercountstats**.
+1. Create a new code cell and enter the following code. We'll analyze this data and save the results into a table called **nyctaxi.passengercountstats**.
 
    ```py
    %%pyspark

articles/synapse-analytics/sql/on-demand-workspace-overview.md

@@ -7,18 +7,18 @@ ms.service: synapse-analytics
 ms.topic: overview
 ms.subservice: sql
 ms.custom: ignite-2022
-ms.date: 01/19/2022
+ms.date: 10/11/2022
 ms.author: fipopovi
 ms.reviewer: sngun
 ---
-# Serverless SQL pool in Azure Synapse Analytics 
+# Serverless SQL pool in Azure Synapse Analytics
 
 Every Azure Synapse Analytics workspace comes with serverless SQL pool endpoints that you can use to query data in the [Azure Data Lake](query-data-storage.md) ([Parquet](query-data-storage.md#query-parquet-files), [Delta Lake](query-delta-lake-format.md), [delimited text](query-data-storage.md#query-csv-files) formats), [Azure Cosmos DB](query-cosmos-db-analytical-store.md?toc=%2Fazure%2Fsynapse-analytics%2Ftoc.json&bc=%2Fazure%2Fsynapse-analytics%2Fbreadcrumb%2Ftoc.json&tabs=openrowset-key), or Dataverse.
 
 Serverless SQL pool is a query service over the data in your data lake. It enables you to access your data through the following functionalities:
- 
-- A familiar [T-SQL syntax](overview-features.md) to query data in place without the need to copy or load data into a specialized store. 
-- Integrated connectivity via the T-SQL interface that offers a wide range of business intelligence and ad-hoc querying tools, including the most popular drivers. 
+
+- A familiar [T-SQL syntax](overview-features.md) to query data in place without the need to copy or load data into a specialized store. To learn more, see the [T-SQL support](#t-sql-support) section.
+- Integrated connectivity via the T-SQL interface that offers a wide range of business intelligence and ad-hoc querying tools, including the most popular drivers. To learn more, see the [Client tools](#client-tools) section.
 
 Serverless SQL pool is a distributed data processing system, built for large-scale data and computational functions. Serverless SQL pool enables you to analyze your Big Data in seconds to minutes, depending on the workload. Thanks to built-in query execution fault-tolerance, the system provides high reliability and success rates even for long-running queries involving large data sets.