to address issue https://github.com/MicrosoftDocs/azure-docs/issues/60004

Author: TheovanKraay

articles/cosmos-db/cassandra-spark-ddl-ops.md

@@ -1,13 +1,13 @@
 ---
 title: DDL operations in Azure Cosmos DB Cassandra API from Spark
 description: This article details keyspace and table DDL operations against Azure Cosmos DB Cassandra API from Spark.
-author: kanshiG
-ms.author: govindk
+author: TheovanKraay
+ms.author: thvankra
 ms.reviewer: sngun
 ms.service: cosmos-db
 ms.subservice: cosmosdb-cassandra
 ms.topic: how-to
-ms.date: 09/24/2018
+ms.date: 10/07/2020
 
 ---
 
@@ -88,8 +88,7 @@ DESCRIBE keyspaces;
 ### Create a table
 
 ```scala
-val cdbConnector = CassandraConnector(sc)
-cdbConnector.withSessionDo(session => session.execute("CREATE TABLE IF NOT EXISTS books_ks.books(book_id TEXT PRIMARY KEY,book_author TEXT, book_name TEXT,book_pub_year INT,book_price FLOAT) WITH cosmosdb_provisioned_throughput=4000 , WITH default_time_to_live=630720000;"))
+cdbConnector.withSessionDo(session => session.execute("CREATE TABLE IF NOT EXISTS books_ks1.books(book_id TEXT,book_author TEXT, book_name TEXT,book_pub_year INT,book_price FLOAT, PRIMARY KEY(book_id,book_pub_year)) WITH cosmosdb_provisioned_throughput=4000 , WITH default_time_to_live=630720000;"))
 ```
 
 #### Validate in cqlsh

articles/cosmos-db/cassandra-spark-read-ops.md

@@ -81,17 +81,10 @@ readBooksDF.show
 You can push down predicates to the database to allow for better optimized Spark queries. A predicate is a condition on a query that returns true or false, typically located in the WHERE clause. A predicate push down filters the data in the database query, reducing the number of entries retrieved from the database and improving query performance. By default the Spark Dataset API will automatically push down valid WHERE clauses to the database. 
 
 ```scala
-val readBooksDF = spark
-  .read
-  .format("org.apache.spark.sql.cassandra")
-  .options(Map( "table" -> "books", "keyspace" -> "books_ks"))
-  .load
-  .select("book_name","book_author", "book_pub_year")
-  .filter("book_pub_year > 1891")
-//.filter("book_name IN ('A sign of four','A study in scarlet')")
-//.filter("book_name='A sign of four' OR book_name='A study in scarlet'")
-//.filter("book_author='Arthur Conan Doyle' AND book_pub_year=1890")
-//.filter("book_pub_year=1903")  
+val df = spark.read.cassandraFormat("books", "books_ks").load
+df.explain
+val dfWithPushdown = df.filter(df("book_pub_year") > 1891)
+dfWithPushdown.explain
 
 readBooksDF.printSchema
 readBooksDF.explain