[Doc]update getting started doc for EMR and databricks[skip ci]

docs/get-started/getting-started-aws-emr.md

@@ -14,6 +14,7 @@ Different versions of EMR ship with different versions of Spark, RAPIDS Accelera
 
 | EMR | Spark | RAPIDS Accelerator jar | cuDF jar | xgboost4j-spark jar
 | --- | --- | --- | ---| --- |
+| 6.9 | 3.3.0 | rapids-4-spark_2.12-22.08.0.jar | Bundled with rapids-4-spark | xgboost4j-spark_3.0-1.4.2-0.3.0.jar |
 | 6.8 | 3.3.0 | rapids-4-spark_2.12-22.06.0.jar | Bundled with rapids-4-spark | xgboost4j-spark_3.0-1.4.2-0.3.0.jar |
 | 6.7 | 3.2.1 | rapids-4-spark_2.12-22.02.0.jar | cudf-22.02.0-cuda11.jar | xgboost4j-spark_3.0-1.2.0-0.1.0.jar |
 | 6.6 | 3.2.0 | rapids-4-spark_2.12-22.02.0.jar | cudf-22.02.0-cuda11.jar | xgboost4j-spark_3.0-1.2.0-0.1.0.jar |
@@ -40,7 +41,7 @@ g4dn.2xlarge nodes:
 
 ```
 aws emr create-cluster \
---release-label emr-6.7.0 \
+--release-label emr-6.9.0 \
 --applications Name=Hadoop Name=Spark Name=Livy Name=JupyterEnterpriseGateway \
 --service-role EMR_DefaultRole \
 --ec2-attributes KeyName=my-key-pair,InstanceProfile=EMR_EC2_DefaultRole \
@@ -80,8 +81,8 @@ detailed cluster configuration page.
 
 #### Step 1:  Software Configuration and Steps
 
-Select **emr-6.8.0** for the release, uncheck all the software options, and then check **Hadoop
-3.2.1**, **Spark 3.3.0**, **Livy 0.7.1** and **JupyterEnterpriseGateway 2.1.0**.
+Select **emr-6.9.0** for the release, uncheck all the software options, and then check **Hadoop
+3.3.3**, **Spark 3.3.0**, **Livy 0.7.1** and **JupyterEnterpriseGateway 2.6.0**.
 
 In the "Edit software settings" field, copy and paste the configuration from the [EMR
 document](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-rapids.html). You can also

docs/get-started/getting-started-databricks.md

@@ -55,18 +55,17 @@ The number of GPUs per node dictates the number of Spark executors that can run
 	detected.
 
 ## Start a Databricks Cluster
-Create a Databricks cluster by going to Clusters, then clicking `+ Create Cluster`.  Ensure the
+Create a Databricks cluster by going to "Compute", then clicking `+ Create compute`.  Ensure the
 cluster meets the prerequisites above by configuring it as follows:
 1. Select the Databricks Runtime Version from one of the supported runtimes specified in the
    Prerequisites section.
-2. Under Autopilot Options, disable autoscaling.
-3. Choose the number of workers that matches the number of GPUs you want to use.
-4. Select a worker type.  On AWS, use nodes with 1 GPU each such as `p3.2xlarge` or `g4dn.xlarge`.
+2. Choose the number of workers that matches the number of GPUs you want to use.
+3. Select a worker type. On AWS, use nodes with 1 GPU each such as `p3.2xlarge` or `g4dn.xlarge`.
    p2 nodes do not meet the architecture requirements (Pascal or higher) for the Spark worker
    (although they can be used for the driver node).  For Azure, choose GPU nodes such as
-   Standard_NC6s_v3.  For GCP, choose N1 or A2 instance types with GPUs. 
-5. Select the driver type. Generally this can be set to be the same as the worker.
-6. Start the cluster.
+   Standard_NC6s_v3. For GCP, choose N1 or A2 instance types with GPUs. 
+4. Select the driver type. Generally this can be set to be the same as the worker.
+5. Start the cluster.
 
 ## Advanced Cluster Configuration
 
@@ -93,25 +92,25 @@ cluster.
 2. Once you are in the notebook, click the “Run All” button.
 3. Ensure that the newly created init.sh script is present in the output from cell 2 and that the
    contents of the script are correct.
-4. Go back and edit your cluster to configure it to use the init script.  To do this, click the
-   “Clusters” button on the left panel, then select your cluster.
-5. Click the “Edit” button, then navigate down to the “Advanced Options” section.  Select the “Init
+4. Go back and edit your cluster to configure it to use the init script. To do this, click the
+   “Compute” button on the left panel, then select your cluster.
+5. Click the “Edit” button, then navigate down to the “Advanced Options” section. Select the “Init
    Scripts” tab in the advanced options section, and paste the initialization script:
    `dbfs:/databricks/init_scripts/init.sh`, then click “Add”.
 
     ![Init Script](../img/Databricks/initscript.png)
 
 6. Now select the “Spark” tab, and paste the following config options into the Spark Config section.
-   Change the config values based on the workers you choose.  See Apache Spark
+   Change the config values based on the workers you choose. See Apache Spark
    [configuration](https://spark.apache.org/docs/latest/configuration.html) and RAPIDS Accelerator
    for Apache Spark [descriptions](../configs.md) for each config.
 
     The
     [`spark.task.resource.gpu.amount`](https://spark.apache.org/docs/latest/configuration.html#scheduling)
     configuration is defaulted to 1 by Databricks. That means that only 1 task can run on an
-    executor with 1 GPU, which is limiting, especially on the reads and writes from Parquet.  Set
+    executor with 1 GPU, which is limiting, especially on the reads and writes from Parquet. Set
     this to 1/(number of cores per executor) which will allow multiple tasks to run in parallel just
-    like the CPU side.  Having the value smaller is fine as well.
+    like the CPU side. Having the value smaller is fine as well.
     Note: Please remove the `spark.task.resource.gpu.amount` config for a single-node Databricks 
     cluster because Spark local mode does not support GPU scheduling.
 
@@ -184,7 +183,7 @@ output_path='dbfs:///FileStore/tables/mortgage_parquet_gpu/output/'
 Run the notebook by clicking “Run All”. 
 
 ## Hints
-Spark logs in Databricks are removed upon cluster shutdown.  It is possible to save logs in a cloud
+Spark logs in Databricks are removed upon cluster shutdown. It is possible to save logs in a cloud
 storage location using Databricks [cluster log
 delivery](https://docs.databricks.com/clusters/configure.html#cluster-log-delivery-1).  Enable this
 option before starting the cluster to capture the logs.