Show an example of similarities and differences between Databricks CLI used between Azure and Databricks on AWS and Google

Similarities:

• Basic commands and structure: Both CLIs share similar fundamental commands like clusters, jobs, fs, and libraries for basic cluster management, job execution, file system interaction, and library management.
• Spark functionalities: Both platforms offer access to the core Spark capabilities for data processing and analytics through CLI commands like spark-submit and spark-sql.
• Output format: Both CLIs display output in similar formats like text, JSON, and tables, making results readily interpretable.

Differences:

• Command names and parameters: Some commands and their parameters have different names or slightly different usage syntax. For example, clusters list in Azure becomes jobs list-clusters in AWS and GCP.
• API endpoints: Azure uses Azure-specific API endpoints like https://management.azure.com/ while AWS and GCP use their own endpoints like https://databricks.aws and https://databricks.gcp.io. You need to modify scripts to use the appropriate endpoints for each platform.
• Authentication mechanisms: Azure uses AAD for authentication, requiring different commands like az login compared to AWS CLI's aws configure and GCP CLI's gcloud auth login.
• Resource names and identifiers: Cluster IDs, workspace names, and other resources have different naming conventions. Azure uses /workspaces/ while AWS uses /clusters/ and GCP uses /jobs/. Scripts need to adjust resource references accordingly.

Here's an example of a simple spark-submit command showcasing some differences:

Azure:

                    databricks clusters submit-job \
                    --cluster-id <cluster-id> \
                    --jar /path/to/jar.jar \
                    --class com.example.MySparkApp \
                    --conf spark.driver.memory=8g
                

AWS:

                    aws databricks jobs run-spark-task-jar \
                    --cluster-id <cluster-id> \
                    --jar-uri /path/to/jar.jar \
                    --main-class com.example.MySparkApp \
                    --spark-submit-params="--driver-memory 8g"
                

GCP:

                    gcloud databricks jobs run-spark-task-jar \
                    --cluster-id <cluster-id> \
                    --jar-uri /path/to/jar.jar \
                    --main-class com.example.MySparkApp \
                    --spark-submit-options="--driver-memory 8g"
                

As you can see, the core functionality (submitting a spark job) remains similar, but the command names, parameters, and syntax differ for each platform.