Merge pull request #7222 from NelGson/vnext

Removed "Next steps" in security concepts

Author: rothja

docs/big-data-cluster/concept-security.md

@@ -55,29 +55,6 @@ Username for accessing the HDFS/Spark endpoint:
 
 Password for accessing the HDFS/Spark endpoint:
  + KNOX_PASSWORD=<knox_password>
- 
-### Update HDFS/Spark (Knox) and Controller passwords
-In order to update Knox and controller admin passwords, the following command can be used:
-
-   ```bash
-    mssqlctl set-password service [-h] [-v] servicename clustername
-   ```
-
-  Service name indicated which service to update the password for. This can be either "knox" or "controller"
-
-
-Optional arguments:
- *  -h, --help     show this help message and exit
- * -v, --verbose  Increase output verbosity
-
-For example, to update the Knox password for a cluster called "testcluster", run the following:
-
-  ```bash
-export KNOX_PASSWORD = <password>
-mssqlctl set-password service knox testcluster
-   ```
-
-It is currently not possible to change the Knox user name.
 
 ## Intra cluster authentication
 
@@ -93,6 +70,4 @@ Please note that in CTP2.0, only basic authentication is supported. Fine-grained
 
 Communication with non-SQL services within the Big Data Cluster, like for example Livy to Spark or Spark to Storage Pool, is secured using certificates. All SQL Sever to SQL Server communication is secured using SQL logins.
 
-## Next steps
-
-TBD
+## Next step

docs/big-data-cluster/ml-on-master.md

@@ -1,8 +1,8 @@
 ---
 title: Machine Language services on the master instance of SQL Server Big Data Cluster | Microsoft Docs
 description:
-author: rothja 
-ms.author: jroth 
+author: NelGson 
+ms.author: ms.author:negust  
 manager: craigg
 ms.date: 08/27/2018
 ms.topic: conceptual
@@ -11,7 +11,29 @@ ms.prod: sql
 
 # Machine Language services on the master instance of SQL Server Big Data Cluster
 
-TBD
+SQL Server Machine Learning Services is an add-on feature to the database engine, used for executing R and Python code in SQL Server. This feature is based on the SQL Server extensibility framework, which isolates external processes from core engine processes, but fully integrates with the relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL.
+
+As part of SQL Server Big Data Cluster, this feature will be available from the Master Instance by default. This means that once external script execution is enabled on the Master Instance, it is going to be possible to execute R and Python scripts using sp_execute_external_script.
+
+To read more about SQL Server Machine Learning Services, please visit the existing documentation on the topic here. TODO: LINK
+
+## Advantages of Machine Learning Services in Big Data Cluster
+
+SQL Server 2019 makes it easy for big data sets to be joined to the dimensional and fact data (high value data) typically stored in the enterprise database. The value of the big data greatly increases when it is not just in the hands of parts of an organization, but is also included in reports, dashboards, and applications. At the same time, the data scientists can continue to use the Spark/HDFS ecosystem tools and have easy, real time access to the high-value data in SQL Server.
+With SQL Server 2019 Big Data Clusters, existing customers can do more with their enterprise data lakes. SQL developers and analysts can:
+
+* Build applications consuming enterprise data lakes.
+* Reason over all data with Transact-SQL queries.
+* Use the existing ecosystem of SQL Server tools and applications to access and analyze enterprise data.
+* Reduce the need for data movement through data virtualization and data marts in HDFS.
+* Big data engineers and data scientists can:
+* Continue to use Spark for big data scenarios.
+* Build intelligent enterprise applications using:
+* Spark to train models over data lakes.
+* Operationalize models in production databases for best performance.
+* Stream data directly into Enterprise data marts for real-time analytics.
+* Explore data visually using interactive analysis and BI tools.
+
 
 ## Next steps