Update README.md

Author: spoddutur

README.md

@@ -5,10 +5,11 @@ This project shows how to use SPARK as Cloud-based SQL Engine and expose your bi
 Traditional relational Database engines like SQL had scalability problems and so evolved couple of SQL-on-Hadoop frameworks like Hive, Cloudier Impala, Presto etc. These frameworks are essentially cloud-based solutions and they all come with their own advantages and limitations. This project will demo how SparkSQL comes across as one more SQL-on-Hadoop framework as listed below:
 - Data from multiple sources can be pushed into Spark and then exposed as SQLtable
 - These tables are then made accessible as a JDBC/ODBC data source via the Spark thrift server.
-- Multiple clients like ```Beeline CLI```, ```JDBC```, ```ODBC``` or ```BI tools like Tableau``` connect to Spark thrift server. - Once the connection is established, ThriftServer will contact SparkSQL engine to access Hive or Spark temp tables and run the sql queries on ApacheSpark framework.
+- Multiple clients like ```Beeline CLI```, ```JDBC```, ```ODBC``` or ```BI tools like Tableau``` connect to Spark thrift server.
+- Once the connection is established, ThriftServer will contact ```SparkSQL engine to access Hive or Spark temp tables and run the sql queries on ApacheSpark framework```.
 - Spark Thrift basically works similar to HiveServer2 thrift where HiveServer2 submits the sql queries as Hive MapReduce job vs Spark thrift server will use Spark SQL engine which underline uses full spark capabilities.
 
-#### Complete Guide - To know more about this topic, please refer to my blog [here](https://spoddutur.github.io/spark-notes/spark-as-cloud-based-sql-engine-via-thrift-server) where I briefed the concept in detail.
+#### To know more about this topic, please refer to my blog [here](https://spoddutur.github.io/spark-notes/spark-as-cloud-based-sql-engine-via-thrift-server) where I briefed the concept in detail.
 
 ### Architecture
 Following picture illustrates the idea we discussed above: