Update references from mssqlctl to azdata

samples/features/sql-big-data-cluster/app-deploy/README.md

@@ -7,10 +7,10 @@ Application deployment allows you to deploy applications into SQL Server big dat
 
 ## Pre-requisites
 * SQL Server big data cluster CTP 2.3 or later
-* `mssqlctl` CLI familiarity. If you are unfamiliar with `mssqlctl` please refer to - [App Deployment in SQL Server big data cluster](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) for more information.
+* `azdata` CLI familiarity. If you are unfamiliar with `azdata` please refer to - [App Deployment in SQL Server big data cluster](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) for more information.
 
 * Tip 
-**mssqlctl app -h** will display the various commands to manage the app
+**azdata app -h** will display the various commands to manage the app
 
 ## Templates
 Templates are used by our [App Deploy add-ins](https://docs.microsoft.com/en-us/sql/big-data-cluster/app-deployment-extension?view=sqlallproducts-allversions) and can be used to quickly deploy applications.

samples/features/sql-big-data-cluster/app-deploy/RollDice/README.md

@@ -34,7 +34,7 @@ To run this sample, you need the following prerequisites.
 **Software prerequisites:**
 
 1. SQL Server big data cluster CTP 2.3 or later.
-2. `mssqlctl`. Refer to [installing mssqlctl](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-mssqlctl?view=sqlallproducts-allversions) document on setting up the `mssqlctl` and connecting to a SQL Server 2019 big data cluster.
+2. `azdata`. Refer to [installing azdata](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-azdata?view=sqlallproducts-allversions) document on setting up the `azdata` and connecting to a SQL Server 2019 big data cluster.
 
 <a name=run-this-sample></a>
 
@@ -44,20 +44,20 @@ To run this sample, you need the following prerequisites.
 2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    mssqlctl login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `roll-dice.R` files are located:
     ```bash
-    mssqlctl app create --spec ./RollDice
+    azdata app create --spec ./RollDice
     ```
 4. Check the deployment by running the following command:
     ```bash
-    mssqlctl app list -n roll-dice -v [version]
+    azdata app list -n roll-dice -v [version]
     ```
     Once the app is listed as `Ready` you can continue to the next step.
 5. Test the app by running the following command:
     ```bash
-    mssqlctl app run -n roll-dice -v [version] --input x=3
+    azdata app run -n roll-dice -v [version] --input x=3
     ```
     You should get output like the example for three dice below. The results of the dice rolled are in the `result` data frame:
     ```json
@@ -88,7 +88,7 @@ To run this sample, you need the following prerequisites.
 6. You can clean up the sample by running the following commands:
     ```bash
     # delete app
-    mssqlctl app delete --name roll-dice --version [version]
+    azdata app delete --name roll-dice --version [version]
     ```
 
 <a name=sample-details></a>

samples/features/sql-big-data-cluster/app-deploy/SSIS/README.md

@@ -23,7 +23,7 @@ To run this sample, you need the following prerequisites.
 **Software prerequisites:**
 
 1. SQL Server big data cluster CTP 2.3 or later.
-2. `mssqlctl`. Refer to [installing mssqlctl](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-mssqlctl?view=sqlallproducts-allversions) document on setting up the `mssqlctl` and connecting to a SQL Server big data cluster.
+2. `azdata`. Refer to [installing azdata](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-azdata?view=sqlallproducts-allversions) document on setting up the `azdata` and connecting to a SQL Server big data cluster.
 3. Optional: to see the SSIS package itself, install Visual Studio 2017 if you don't have it already. After that download and install [SSDT](https://docs.microsoft.com/en-us/sql/ssdt/download-sql-server-data-tools-ssdt?view=sql-server-2017#ssdt-for-vs-2017-standalone-installer). 
 4. Optional: install [SSMS](https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017) if it is not already installed.
 
@@ -35,16 +35,16 @@ To run this sample, you need the following prerequisites.
 2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    mssqlctl login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
     ```
 3. Replace `[SA_PASSWORD]` in the `spec.yaml` file with the password for SQL user `sa`.
 4. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `back-up-db.dtsx` files are located:
     ```bash
-    mssqlctl app create --spec ./SSIS
+    azdata app create --spec ./SSIS
     ```
 5. Check the deployment by running the following command:
     ```bash
-    mssqlctl app list --name back-up-db --version [version]
+    azdata app list --name back-up-db --version [version]
     ```
     Once the app is listed as `Ready` the job should run within a minute.
     You can check if the backup is created by running:
@@ -56,7 +56,7 @@ To run this sample, you need the following prerequisites.
 6. You can clean up the sample by running the following commands:
     ```bash
     # delete app
-    mssqlctl app delete --name back-up-db --version [version]
+    azdata app delete --name back-up-db --version [version]
     # delete backup files
     kubectl -n [your namespace] exec -it mssql-master-pool-0 -c mssql-server -- /bin/bash -c "rm /var/opt/mssql/data/*.DWConfigbak"
     ```
@@ -73,7 +73,7 @@ Here is the spec file for this application. This sample uses the `SSIS` runtime
 |Setting|Description|
 |-|-|
 |options|Specifies any command line parameters passed to the execution of the SSIS package|
-|schedule|Specifies when the job should run. This follows cron expressions. A value of '*/1 * * * *' means the job runs *every minute*. If omitted the package will not run automatically and you can run the package on demand using `mssqlctl run -n back-up-db -v [version]` or making a call to the API.|
+|schedule|Specifies when the job should run. This follows cron expressions. A value of '*/1 * * * *' means the job runs *every minute*. If omitted the package will not run automatically and you can run the package on demand using `azdata run -n back-up-db -v [version]` or making a call to the API.|
 
 ```yaml
 name: back-up-db

samples/features/sql-big-data-cluster/app-deploy/addpy/README.md

@@ -35,7 +35,7 @@ To run this sample, you need the following prerequisites.
 **Software prerequisites:**
 
 1. SQL Server big data cluster CTP 2.3 or later.
-2. `mssqlctl`. Refer to [installing mssqlctl](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-mssqlctl?view=sqlallproducts-allversions) document on setting up the `mssqlctl` and connecting to a SQL Server 2019 big data cluster.
+2. `azdata`. Refer to [installing azdata](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-azdata?view=sqlallproducts-allversions) document on setting up the `azdata` and connecting to a SQL Server 2019 big data cluster.
 
 <a name=run-this-sample></a>
 
@@ -45,20 +45,20 @@ To run this sample, you need the following prerequisites.
 2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    mssqlctl login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `add.py` files are located:
     ```bash
-    mssqlctl app create --spec ./addpy
+    azdata app create --spec ./addpy
     ```
 4. Check the deployment by running the following command:
     ```bash
-    mssqlctl app list -n addpy -v [version]
+    azdata app list -n addpy -v [version]
     ```
     Once the app is listed as `Ready` you can continue to the next step.
 5. Test the app by running the following command:
     ```bash
-    mssqlctl app run -n addpy -v [version] --input x=3,y=5
+    azdata app run -n addpy -v [version] --input x=3,y=5
     ```
     You should get output like the example below. The result of adding 3+5 are returned as `result`.
     ```json
@@ -75,7 +75,7 @@ To run this sample, you need the following prerequisites.
     ```
 6. <a name=restapi></a>Any app you create is also accessible using a RESTful web service that is [Swagger](swagger.io) compliant. You can get the endpoint for the web service by running:
    ```bash
-   mssqlctl app describe --name addpy --version [version]
+   azdata app describe --name addpy --version [version]
    ```
    This will return an output much like the following:
    ```json
@@ -106,28 +106,28 @@ To run this sample, you need the following prerequisites.
     }
    ```
    Note the IP address and the port number in this output. Open the following URL in your browser:
-   `https://[IP]:[PORT]/api/docs/swagger.json`. You will have to log in with the same credentials you used for `mssqlctl login`. The contents of the `swagger.json` you can paste into [Swagger Editor](https://editor.swagger.io) to understand what methods are available:
+   `https://[IP]:[PORT]/api/docs/swagger.json`. You will have to log in with the same credentials you used for `azdata login`. The contents of the `swagger.json` you can paste into [Swagger Editor](https://editor.swagger.io) to understand what methods are available:
    ![API Swagger](api_swagger.png)
 
    Notice the `app` GET method as well as the `token` POST method. Since the authentication for apps uses JWT tokens you will need to get a token my using your favorite tool to make a POST call to the `token` method. Here is an example of how to do just that in [Postman](https://www.getpostman.com/):
    ![Postman Token](postman_token.png)
 
    The result of this request will give you an `access_token`, which you will need to call the URL to run the app.
 
-   > *Optional*: If you want, you can open the URL for the `swagger` that was returned when you ran `mssqlctl app describe --name addpy --version [version]` in your browser. You will have to log in with the same credentials you used for `mssqlctl login`. The contents of the `swagger.json` you can paste into [Swagger Editor](https://editor.swagger.io). You will see that the web service exposes the `run` method.
+   > *Optional*: If you want, you can open the URL for the `swagger` that was returned when you ran `azdata app describe --name addpy --version [version]` in your browser. You will have to log in with the same credentials you used for `azdata login`. The contents of the `swagger.json` you can paste into [Swagger Editor](https://editor.swagger.io). You will see that the web service exposes the `run` method.
 
    You can use your favorite tool to call the `run` method (`https://[IP]:30778/api/app/addpy/[version]/run`), passing in the parameters in the body of your POST request as json. In this example we will use [Postman](https://www.getpostman.com/). Before making the call, you will need to set the `Authorization` to `Bearer Token` and paste in the token you retrieved earlier. This will set a header on your request. See the screenshot below.
    ![Postman Run Headers](postman_run_1.png)
    Next, in the requests body, pass in the parameters to the app you are calling and set the `content-type` to `application/json`:
    ![Postman Run Body](postman_run_2.png)
-   When you send the request, you will get the same output as you did when you ran the app through `mssqlctl app run`:
+   When you send the request, you will get the same output as you did when you ran the app through `azdata app run`:
    ![Postman Run Result](postman_result.png)
    You have now successfully called the app through the web service!
 
 7. You can clean up the sample by running the following commands:
     ```bash
     # delete app
-    mssqlctl app delete --name addpy --version [version]
+    azdata app delete --name addpy --version [version]
     ```
 
 <a name=sample-details></a>

samples/features/sql-big-data-cluster/app-deploy/magic8ball/README.md

@@ -34,7 +34,7 @@ To run this sample, you need the following prerequisites.
 **Software prerequisites:**
 
 1. SQL Server big data cluster CTP 2.3 or later.
-2. `mssqlctl`. Refer to [installing mssqlctl](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-mssqlctl?view=sqlallproducts-allversions) document on setting up the `mssqlctl` and connecting to a SQL Server 2019 big data cluster.
+2. `azdata`. Refer to [installing azdata](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-azdata?view=sqlallproducts-allversions) document on setting up the `azdata` and connecting to a SQL Server 2019 big data cluster.
 
 <a name=run-this-sample></a>
 
@@ -44,20 +44,20 @@ To run this sample, you need the following prerequisites.
 2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
     ```bash
-    mssqlctl login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+    azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
     ```
 3. Deploy the application by running the following command, specifying the folder where your `spec.yaml` and `magic8ball.py` files are located:
     ```bash
-    mssqlctl app create --spec ./magic8ball
+    azdata app create --spec ./magic8ball
     ```
 4. Check the deployment by running the following command:
     ```bash
-    mssqlctl app list -n magic8ball -v [version]
+    azdata app list -n magic8ball -v [version]
     ```
     Once the app is listed as `Ready` you can continue to the next step.
 5. Test the app by running the following command:
     ```bash
-    mssqlctl app run -n magic8ball -v [version] --input txt="Will it rain tomorrow?"
+    azdata app run -n magic8ball -v [version] --input txt="Will it rain tomorrow?"
     ```
     You should get output like the example below. The answer to your question are returned as `result`.
     ```json
@@ -78,7 +78,7 @@ To run this sample, you need the following prerequisites.
 6. You can clean up the sample by running the following commands:
     ```bash
     # delete app
-    mssqlctl app delete --name magic8ball --version [version]
+    azdata app delete --name magic8ball --version [version]
     ```
 
 <a name=sample-details></a>

samples/features/sql-big-data-cluster/app-deploy/mleap/README.md

@@ -31,7 +31,7 @@ To run this sample, you need the following prerequisites.
 **Software prerequisites:**
 
 1. SQL Server big data cluster CTP 2.3 or later.
-2. `mssqlctl`. Refer to [installing mssqlctl](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-mssqlctl?view=sqlallproducts-allversions) document on setting up the `mssqlctl` and connecting to a SQL Server 2019 big data cluster.
+2. `azdata`. Refer to [installing azdata](https://docs.microsoft.com/en-us/sql/big-data-cluster/deploy-install-azdata?view=sqlallproducts-allversions) document on setting up the `azdata` and connecting to a SQL Server 2019 big data cluster.
 
 <a name=run-this-sample></a>
 
@@ -41,25 +41,25 @@ To run this sample, you need the following prerequisites.
 2. Log in to the SQL Server big data cluster using the command below using the IP address of the `mgmtproxy-svc-external` in your cluster. If you are not familiar with `mssqltctl` you can refer to the [documentation](https://docs.microsoft.com/en-us/sql/big-data-cluster/big-data-cluster-create-apps?view=sqlallproducts-allversions) and then return to this sample.
 
    ```bash
-   mssqlctl login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
+   azdata login -e https://<ip-address-of-mgmtproxy-svc-external>:30777 -u <user-name> -p <password>
    ```
 3. This example uses a TensorFlow Machine Learning Model that uses public US Census data predict income. [More details and information on the example are here](https://docs.microsoft.com/en-us/sql/big-data-cluster/train-and-create-machinelearning-models-with-spark?view=sqlallproducts-allversions). The application you will be deploying as part of this sample is a Random Forest Model that was built in Spark and has been [serialized as an MLeap bundle](https://docs.microsoft.com/en-us/sql/big-data-cluster/export-model-with-spark-mleap?view=sqlallproducts-allversions).
 
    Deploy the app using the `create` command and pass the location of the spec file. In the example below, the spec file is expected to be in the `mleap` folder: 
    ```bash
-   mssqlctl app create --spec ./mleap/
+   azdata app create --spec ./mleap/
    ```
 1. Check the deployment by running the following command:
    ```bash
-   mssqlctl app list -n mleap-census -v [version]
+   azdata app list -n mleap-census -v [version]
    ```
    Once the app is listed as `Ready` you can continue to the next step.
 2. Now that the app has been deployed you can test if the app works correctly by passing in a sample input that is available in the `mleap` folder. The deployed app is a RESTful webservice that is [Swagger](swagger.io) compliant. For this sample we will show you how you can test this using the CLI.
 
    To test the app, run the command below. The input parameter is a `MLeapFrame`, a `json` file that describes the parameters and the values provided to the model for predicting income. Note that the input parameter has a special character '@' to indicate that a `json` file is being passed. This command needs to be run within the `mleap` folder. 
 
    ```bash
-   mssqlctl app run --name mleap-census --version [version] --input schema=@census_frame.json
+   azdata app run --name mleap-census --version [version] --input schema=@census_frame.json
    ```
 
    The result will be a json output that includes the predicted income along with additional data.
@@ -69,7 +69,7 @@ To run this sample, you need the following prerequisites.
 6. You can clean up the sample by running the following commands:
    ```bash
    # delete app
-   mssqlctl app delete --name mleap-census --version [version]
+   azdata app delete --name mleap-census --version [version]
    ```
 
 <a name=sample-details></a>