fix(tutorials): align code-fence indentation to valid CommonMark (step 3)

tutorials/create-hello-world-mta/create-hello-world-mta.md

@@ -1,4 +1,4 @@
 ---
 parser: v2
 auto_validation: true
 time: 20
@@ -96,7 +96,7 @@
 
 2. Using a text editor, enter the following data in the file:
 
-   ```YAML
+```YAML
 _schema-version: '3.1'
 ID: com.example.demo.basic.config
 extends: com.example.demo.basic
@@ -109,7 +109,7 @@
        id: dbalias
        user-id: myuser
        password : mypassword
-   ```
+```
 
     The example above instructs the SAP BTP to:
 

tutorials/hana-cloud-collaborative-database-development-5/hana-cloud-collaborative-database-development-5.md

@@ -1,4 +1,4 @@
 ---
 parser: v2
 auto_validation: true
 time: 15
@@ -198,22 +198,22 @@
 
 3.	In the SQL Console, copy and paste the following code. Run it to create a role for the object owner (OO).
 
-   ```SQL
+```SQL
 create role "genericRoleForOO";
 ```
     > The Object Owner, in this example, will be assigned to the user who owns the project and creates calculation views. This role will need Select privileges with grant option that will be assigned to it in the next steps.
 
 4.	Next, copy and paste the following code in the SQL Console and run it to create a role for application users (AP).
 
-   ```SQL
+```SQL
 create role "genericRoleForAP";
-	```
+```
 
     > The Application User, in this example, will be assigned to the user who uses the data preview on the calculation views created by the object owner. This role will need Select privileges that will be assigned to it in the next steps.
 
 5.	Copy and paste the following code in the SQL Console. Run the code to view the schema name for the role inventory created earlier in this tutorial group.
 
-   ```SQL
+```SQL
 select role_schema_name,* from roles where role_name='inventory';
 ```
 
@@ -229,23 +229,23 @@
 
 1.	Here, you will assign the two roles you have created in the fourth tutorial ("`inventory_with_grant#`" and "`inventory_without_grant`") to the two new roles you have just created. Copy and paste the following code in the SQL Console and make sure the schema name ("`WS3_1_HDI_DB_1`") matches the schema name you have just checked with the previous statement. Run the statement to grant roles to the object owner and application user.
 
-   ```SQL
+```SQL
 grant "WS3_1_HDI_DB_1"."inventory_with_grant#" to "genericRoleForOO";
 grant "WS3_1_HDI_DB_1"."inventory_without_grant" to "genericRoleForAP";
-	```
+```
 
 2.	Create a database user by running the following SQL statement. `UPS_GRANTOR` means **User-Provided Service Granter**. In the next step, this user will be allowed to grant the roles created above.
 
-   ```SQL
+```SQL
 create user UPS_GRANTOR password "DnATBG!1" NO FORCE_FIRST_PASSWORD_CHANGE;
-	```
+```
 
 3.	Assign the roles you have created to this user `UPS_GRANTOR` by running the statements below.
 
-   ```SQL
+```SQL
 grant  "genericRoleForOO" to UPS_GRANTOR WITH ADMIN OPTION;
 grant  "genericRoleForAP" to UPS_GRANTOR WITH ADMIN OPTION;
-	```
+```
 
 4.	Now the user `UPS_GRANTOR` is assigned both roles that were created previously for collaborating on the project.
 

tutorials/hana-cloud-connection-guide-2/hana-cloud-connection-guide-2.md

@@ -1,4 +1,4 @@
 ---
 parser: v2
 auto_validation: true
 time: 15
@@ -68,22 +68,22 @@
 
 2. Import the packages using the following lines of code:
 
-   ```Python
+```Python
 import hdbcli
 from hdbcli import dbapi
 import hana_ml
 ```
 
 3. To check if the package has been imported correctly, you can check the package version. Just type the following:
 
-   ```Python
+```Python
 print(hdbcli.__version__) #Use two underscores '_''_' for __version__
 print(hana_ml.__version__) #Use two underscores '_''_' for __version__
 ```
 
 4. Next, import the following packages required for this tutorial:
 
-   ```Python
+```Python
 from hana_ml import dataframe
 from hana_ml.dataframe import ConnectionContext
 import pandas as pd
@@ -134,19 +134,19 @@
 
 1. Now that you have a table in your database, to access this table we execute the following lines of code. Replace the XXXX with your schema name and table with your table name.
 
-   ```Python
+```Python
 flight_hdf=conn2.sql ('select * from XXXX.(table) ')
 ```
 
 2. To check the data types of the table, you can execute the following command:
 
-   ```Python
+```Python
 flight_hdf.dtypes()
 ```
 
 3. To explore the SAP HANA data-frame, you can execute the following command:
 
-   ```Python
+```Python
 flight_hdf.describe().collect()
 ```
    ![Access SAP HANA Cloud Table](ss-03-access-SAP-HANA-Cloud-table.png)
@@ -157,13 +157,13 @@
 
 1. To add visualizations to your data, import `EDAVisualizer` using the command:
 
-   ```Python
+```Python
 from hana_ml.visualizers.eda import EDAVisualizer
 ```
 
 2. Next, import the following packages required for this tutorial.
 
-   ```Python
+```Python
 from hana_ml import dataframe
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -176,7 +176,7 @@
 
 3. Now we will create a pie chat using the database table to find the most frequently travelled destination. To create a pie chart using run the following:
 
-   ```Python
+```Python
 f = plt.figure(figsize=(10,10))
 ax1 = f.add_subplot(111)
 start=time.time()

tutorials/hana-cloud-data-anonymization-3/hana-cloud-data-anonymization-3.md

@@ -157,20 +157,20 @@ Once you have filled in all information in this statement, you can run it to cre
 
 1.	To trigger the actual calculation of the required anonymization steps, the view then needs to be refreshed:
 
-   ```SQL
+```SQL
 -- refresh creates the metadata (before, it's not queryable)
 REFRESH VIEW "[view_name]" ANONYMIZATION;
 ```
 
 2.	To show the results of the anonymization, you can use a SELECT statement:
 
-   ```SQL
+```SQL
 -- shows the results
 SELECT * FROM "[view_name]";
 ```
 3.	If you want to double-check if there are no individual cases with unique combinations of quasi-identifiers left in the view, you can run this statement:
 
-   ```SQL
+```SQL
 -- double checks if there are any singled-out ones left --> should be empty
 SELECT "column1", "column2", "column_n", count(*) FROM "[view_name]"
 GROUP BY "column1", "column2", "column_n"
@@ -181,7 +181,7 @@ HAVING count(*)=1;
 
 4.	To examine if your anonymization was successful, you might also check how many people are indistinguishable as specified by the parameter k. To do so, you can run this statement:
 
-   ```SQL
+```SQL
 -- show the actual counts
 SELECT "[column1]", "[column2]", "[column_n]", count(*) as "Count" FROM "[view_name]"
 GROUP BY "[column1]","[column2]","[column_n]"
@@ -212,7 +212,7 @@ If you open the **Explain Plan** of your anonymization view, you can examine the
 
 4. To understand the need for anonymization, run the SQL query on the sample data as given below:
 
-   ```SQL
+```SQL
 SELECT "start_year", "gender", "zipcode", "salary", count(*) as "Count" FROM "ANON_SAMPLE"
 GROUP BY "start_year", "gender", "zipcode", "salary" ORDER by "Count" ASC;
 ```

tutorials/hana-cloud-data-anonymization-4/hana-cloud-data-anonymization-4.md

@@ -53,7 +53,7 @@ To use L-Diversity, you only need to slightly change the statement for creating
 
 4.	You need to add a `COLUMN PARAMETERS` expression defining the type of data in the sensitive column {`"is sensitive":true`}
 
-   ```SQL
+```SQL
 CREATE VIEW "[view_name]" ("id", "[column1]", "[column_n], "[column_sensitive]")
 AS
 SELECT "id", "[column1]", "[column_n], "[column_sensitive]"
@@ -76,7 +76,7 @@ The higher the value you specify for the L-parameter, the more people in each gr
 
 1. First, you must create a sample table `PERSONALDATA` that has sensitive information on people regarding their age. Run the following SQL codes in your SAP HANA Database Explorer.
 
-   ```SQL
+```SQL
 CREATE COLUMN TABLE PERSONALDATA (
  -- sequence column
 ID BIGINT NOT NULL PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
@@ -92,7 +92,7 @@ AGE NVARCHAR(3) NOT NULL);
 
 2. Now, you need to insert information into each column of your table.
 
-   ```SQL
+```SQL
 INSERT INTO PERSONALDATA VALUES ('Peter', 'm', 'Berlin', '30');
 INSERT INTO PERSONALDATA VALUES ('Sigrid', 'f', 'Cologne', '31');
 INSERT INTO PERSONALDATA VALUES ('François', 'm', 'Paris', '24');
@@ -105,7 +105,7 @@ INSERT INTO PERSONALDATA VALUES ('Fabienne', 'f', 'Toulouse', '28');
 
 3. Based on the table created above, here is an example of how a completed statement could look like for an anonymized view on a table that contains two quasi-identifying columns (gender and location) as well as a sensitive age column. In this view, at least `K=2` people should be indistinguishable based on their quasi-identifiers and at least `L=2` people should have different age values:
 
-   ```SQL
+```SQL
 CREATE VIEW PERSONALDATA_L_ANON (ID, GENDER, LOCATION, AGE)
 AS SELECT ID, GENDER, CITY AS LOCATION, AGE
 FROM PERSONALDATA

tutorials/hana-cloud-python-analysis-multimodel-1/hana-cloud-python-analysis-multimodel-1.md

@@ -112,7 +112,7 @@ pandas_df = hana_df.collect()
 
     Add the following content into the file:
 
-   ```Python
+```Python
 {
  "user": "YourUserName",
  "pwd": "YourPassword",

tutorials/hana-cloud-smart-multi-model-2/hana-cloud-smart-multi-model-2.md

@@ -1,4 +1,4 @@
 ---
 parser: v2
 auto_validation: true
 time: 10
@@ -63,19 +63,19 @@
 
 2. Set the schema `DAT260` by executing this statement:
 
-   ```SQL
+```SQL
 SET SCHEMA DAT260;
 ```
 
 3. Paste this statement to the console and execute it:
 
-   ```SQL
+```SQL
 CREATE PREDEFINED SPATIAL REFERENCE SYSTEM IDENTIFIED BY 32630;
 ```
 
 4. A list of all installed Spatial Reference Systems can be found in the table [`ST_SPATIAL_REFERENCE_SYSTEMS`(*)](https://help.sap.com/viewer/bc9e455fe75541b8a248b4c09b086cf5/LATEST/en-US/7a2ea357787c101488ecd1b725836f07.html). To confirm that the above statement has installed `SRS 32630`, you can check the result by executing this statement:
 
-   ```SQL
+```SQL
 SELECT * FROM ST_SPATIAL_REFERENCE_SYSTEMS WHERE SRS_ID = 32630;
 ```
 

tutorials/hana-cloud-smart-multi-model-3/hana-cloud-smart-multi-model-3.md

@@ -1,4 +1,4 @@
 ---
 parser: v2
 auto_validation: true
 time: 10
@@ -49,32 +49,32 @@
 
 3. Next, you need to bring these coordinates into SAP HANA Cloud, SAP HANA database. To do that, you can use the function [`ST_GeomFromText`(*)](https://help.sap.com/viewer/bc9e455fe75541b8a248b4c09b086cf5/LATEST/en-US/7a194a8e787c1014bed49b5134e6b930.html). This constructor for geometries expects a [ **Well-known Text (WKT)** ](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry) as well as the associated spatial reference system as input. Latitude and longitude can be easily assembled to match the expected WKT string. The associated spatial reference system in this case has `id 4326 (WGS84)`.
 
-   ```SQL
+```SQL
 SELECT ST_GeomFromText('POINT(-0.026859 51.505748)', 4326) FROM DUMMY;
-   ```
+```
 
 4. If you use a SQL editor with built-in spatial visualization, like `DBeaver`, you will be able to preview the location and double-check that it matches your previously selected location.
 
     ![Dbeaver.io preview location](ss-03-dbeaver-preview-location.png)
 
 5. To make the following exercises more convenient, it is a good idea to transform this point to the same spatial reference system as our data from OpenStreetMap (SRS with `id 32630`). We can extract the Well-known Text representation by using the function [`ST_AsWKT`(*)](https://help.sap.com/viewer/bc9e455fe75541b8a248b4c09b086cf5/LATEST/en-US/7a169dff787c1014a095b86992806f14.html).
 
-   ```SQL
+```SQL
 SELECT ST_GeomFromText('POINT(-0.026859 51.505748)', 4326) .ST_Transform(32630) .ST_AsWKT() FROM DUMMY;
-   ```
+```
 
 6. In the Results Panel you should now see the transformed coordinates:
 
-   ```
+```
 POINT (706327.107445 5710259.94449)
 ```
 
 
 7. Next, you need to select this point with Spatial Reference System `32630` by executing the following statement.
 
-   ```SQL
+```SQL
 SELECT ST_GeomFromText('POINT (706327.107445 5710259.94449)', 32630) FROM DUMMY;
-   ```
+```
 
 
 ### Select a target POI
@@ -83,31 +83,31 @@
 
 1. Your target destination is a bar called **Blues Kitchen** in Camden. All points of interest are stored in the table `LONDON_POI`. You can search our POI based on field name and amenity. The latter describes the type of POI. In our case we would like to filter for value **bar** by executing this statement:
 
-   ```SQL
+```SQL
 SELECT *
 FROM LONDON_POI lp
 WHERE LOWER("name") LIKE '%blues kitchen%' AND "amenity" = 'bar'
-   ```
+```
 
 2. You should receive 4 records in the **Results** panel.
 
 3. You can use a visualization tool to see the location of these records. If you do not have a visualization tool installed, you can use online tools such as [Wicket](https://arthur-e.github.io/Wicket/sandbox-gmaps3.html). Wicket is able to visualize geometries in WKT format in spatial reference system 4326. To obtain these geometries from SAP HANA Cloud we can use [`ST_Transform`(*)](https://help.sap.com/viewer/bc9e455fe75541b8a248b4c09b086cf5/LATEST/en-US/e2b1e876847a47de86140071ba487881.html)  in combination with [`ST_AsWKT`(*)](https://help.sap.com/viewer/bc9e455fe75541b8a248b4c09b086cf5/LATEST/en-US/7a169dff787c1014a095b86992806f14.html).
 
-   ```SQL
+```SQL
 SELECT "osmid", SHAPE.ST_Transform(4326).ST_AsWKT()
 FROM LONDON_POI lp
 WHERE LOWER("name") LIKE '%blues kitchen%' AND "amenity" = 'bar';
-   ```
+```
 
 4. When you copy over the geometries in WKT format to your (online) visualizer, you will see that two records are not located in Camden and the two other records are actually referring to the same location - one being a polygon depicting the outline of the building and the other being a point depicting the location of the building.
 
     ![Dbeaver.io Camden bar](ss-04-dbeaver-camden-bar.png)
 
 5. In the next step we will work with the POI with OSM ID (field `osmid`) **6274057185**. Select it by executing this statement:
 
-   ```SQL
+```SQL
 SELECT * FROM LONDON_POI lp WHERE "osmid" = 6274057185;
-   ```
+```
 
 
 

tutorials/hana-cloud-smart-multi-model-5/hana-cloud-smart-multi-model-5.md

@@ -66,7 +66,7 @@ Before creating the query, you need to make an adjustment in the SAP HANA Databa
 
 4. Now that this setting has been adjusted, you can start with your query. To perform a spatial union operation and construct one geometry out of the result set of the above query, use the function [`ST_UnionAggr`(*)](https://help.sap.com/viewer/bc9e455fe75541b8a248b4c09b086cf5/LATEST/en-US/601aa9fb93e241af96faafcb8f01b12e.html). The following query creates the SVG as a CLOB:
 
-   ```SQL
+```SQL
 SELECT ST_UnionAggr(le.SHAPE).ST_AsSVG(Attribute=>'stroke="red" stroke-width="0.1%"')
 FROM LONDON_EDGES le
 JOIN LONDON_VERTICES u ON le.SOURCE = u."osmid"