Data Processing Jobs

Determines the authorization credentials that are used to connect Control-M to AWS Data Pipeline.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines one of the following AWS Data Pipeline actions:

• Trigger Pipeline: Runs an existing AWS Data Pipeline.

• Create Pipeline: Creates a new AWS Data Pipeline.

Defines the name of the new AWS Data Pipeline.

Defines the unique ID (idempotency key) that guarantees the pipeline is created only once. After successful execution, this ID cannot be used again.

Valid Characters: Any alphanumeric characters.

Defines the parameter objects, in JSON format, which define the variables for your AWS Data Pipeline.

For more information about the available parameter objects, see the descriptions of the PutPipelineDefinition and GetPipelineDefinition actions in the AWS Data Pipeline API Reference.

"parameterObjects": [ {
   "attributes": [
      {
         "key":"description",
         "stringValue":"S3outputfolder"
      }
    ],
   "id": "myS3OutputLoc"
}
],
"parameterValues": [ {
   "id":"myShellCmd",
   "stringValue":"grep -rc \"GET\" ${IN_DIR}/* > ${OUT_DIR}/out.txt"
}
],
"pipelineObjects": [ {
   "fields": [
      {
         "key":"input",
         "refValue":"S3InputLocation"
      },
      {
         "key":"stage",
         "stringValue":"true"
      }
   ],
   "id":"ShellCommandActivityObj",
   "name":"ShellCommandActivityObj"
}
]

Determines whether to run, or trigger, the newly created AWS Data Pipeline.

Determines which pipeline to run, or trigger.

Determines the number of seconds to wait before checking the status of the Data Pipeline job.

Default: 20

Determines the authorization credentials that are used to connect Control-M to AWS EMR.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Defines the name of the AWS EMR cluster to connect to the Notebook.

In the EMR API, this field is called the Execution Engine ID.

Determines which Notebook ID executes the script.

In the EMR API, this field is called the Editor ID.

Defines the full path and name of the script file in the Notebook.

Defines the job execution name.

Defines the service role to connect to the Notebook.

Enables you to provide Notebook execution information through JSON code.

This JSON Body parameter replaces the values of the following parameters (Cluster ID, Notebook ID, Relative Path, Notebook Execution Name, and Service Role).

Determines the authorization credentials that are used to connect Control-M to Azure Databricks.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

• Variable Name: %%AZURE-ACCOUNT

Determines the ID of the Azure Databricks job that is created in a Databricks workspace.

Defines task parameters to override when the job runs, according to the Databricks convention. Your list of parameters must begin with the name of the parameter type.

"notebook_params":{"param1":"val1", "param2":"val2"}
"jar_params": ["param1", "param2"]

For more information about the parameter types, review the properties of RunParameters in the OpenAPI specification provided in the Azure Databricks documentation.

For no parameters, type the following code:

"params": {}

(Optional) Defines a token to use to rerun job runs that timed out in Databricks.

Values:

• Control-M-Idem_%%ORDERID: With this token, upon rerun, Control-M invokes the monitoring of the existing job run in Databricks. Default.

• Any other value: Replaces the Control-M idempotency token. When you rerun a job using a different token, Databricks creates a new job run with a new unique run ID.

Determines the authorization credentials that are used to connect Control-M to Azure HDInsight.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines which parameters are passed to the Apache Spark Application during job execution, in JSON format (name:value pairs).

This JSON must include the file and className elements.

Determines the number of seconds to wait before the Apache Spark batch job is verified.

Default: 10 seconds

Determines the authorization credentials that are used to connect Control-M to Azure Synapse.

Defines the name of a pipeline that you defined in your Azure Synapse workspace.

Defines pipeline parameters, in JSON format, to override when the job runs.

 {"param1":"value1", "param2":"value2"}

For no parameters, type {}.

Determines the authorization credentials that are used to connect Control-M to Databricks.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines the ID of the Databricks job that is created in a Databricks workspace.

Defines task parameters, in JSON format, to override when the job runs, according to the Databricks convention. Your list of parameters must begin with the name of the parameter type.

"notebook_params":{"param1":"val1", "param2":"val2"}
"jar_params": ["param1", "param2"]

For more information about the parameter types, review the properties of RunParameters in the OpenAPI specification provided through the Azure Databricks documentation.

For no parameters, type the following code:

"params": {}

(Optional) Defines a token to use to rerun job runs that timed out in Databricks.

Values:

• Control-M-Idem_%%ORDERID: With this token, upon rerun, Control-M invokes the monitoring of the existing job run in Databricks. Default.

• Any other value: Replaces the Control-M idempotency token. When you rerun a job using a different token, Databricks creates a new job run with a new unique run ID.

Determines the authorization credentials that are used to connect Control-M to DBT.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Defines the ID of the preexisting job in the DBT platform that you want to run.

Defines a free-text description of the job.

Determines whether to override the predefined DBT job commands.

Defines the new DBT job commands.

Determines the number of seconds to wait before checking the status of the job.

Default: 10

Determines the authorization credentials that are used to connect Control-M to GCP BigQuery.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines the project that the job uses.

Determines the database that the job uses.

Determines one of the following GCP BigQuery actions to perform:

• Query: Runs one or more SQL statements that are supported by GCP BigQuery.

• Copy: Creates a copy of an existing table.

• Load: Loads source data into an existing table.

• Extract: Exports data from an existing table into Google Cloud Storage.

• Routine: Runs a stored procedure, table function, or previously defined function.

(Optional) Determines whether to paste the results of a SELECT statement into a new table.

Defines the new table name.

Defines one or more SQL statements supported by GCP BigQuery.

Rule: It must be written in a single line, with character strings separated by one space only.

Defines the query parameters, in JSON format, which enable you to control the presentation of the data.

{
"name": "IFteam",
"paramterType": { 
"type": "STRING"
	},
"parameterValue": {
"value": "BMC"
	}
}

Determines one of the following copy operations:

• Clone: Creates a copy of a base table that has write access.

• Snapshot: Creates a read-only copy of a base table.

• Copy: Creates a copy of a snapshot.

• Restore: Creates a writable table from a snapshot.

Defines the properties of the table, in JSON format, that is cloned, backed up, or copied.

You can copy or back up one or more tables at a time.

{ 
	{
"datasetID": "Test1", 
"projectID": "SomeProj1",
"tableID": "IFteam1"
	}
	{
"datasetID": "Test2", 
"projectId": "SomepProj2", 
"tableID": "IFteam2"
	}
}

Defines the properties of a new table, in JSON format.

{ 
  "datasetID": "Test3", 
  "projectID": "SomeProj3", 
  "tableID": "IFteam3" 
}

Defines the source or destination data URI for the table that you are loading or extracting.

You can load or extract multiple tables.

You must use commas to distinguish elements from each other.

Determines whether to add more fields to a table that you are loading.

Defines additional fields, in JSON format, for the table that you are loading.

"schema": 
	{
"fields": 
		[
			{
"name": "name1",
"type": "STRING1"
			}
			{
"name": "name2",
"type": "STRING2"
			}
			{
"name": "name3",
"type": "STRING3"
			}
		]
	}

Determines one of the following file formats to export the data to:

• CSV

• JSON

Defines a routine and the values that it must run.

Call new_r(‘value1’)

Determines the maximum number of milliseconds to run the GCP BigQuery job.

Default: 30000 milliseconds (30 seconds)

Determines the number of seconds to wait before the job ends Not OK.

Default: 10

Determines the authorization credentials that are used to connect Control-M to GCP Dataflow.

Defines the project ID for your Google Cloud project.

Defines the Google Compute Engine region to create the job.

Defines one of the following types of GCP Dataflow templates:

• Classic Template: Developers run the pipeline and create a template. The Apache Beam SDK stages files in Cloud Storage, creates a template file (similar to job request), and saves the template file in Cloud Storage.

• Flex Template: Developers package the pipeline into a Docker image and then use the Google Cloud CLI to build and save the Flex Template spec file in Cloud Storage.

Defines the path for temporary files. This must be a valid Google Cloud Storage URL that begins with gs://.

The pipeline option tempLocation is used as the default value, if it has been set.

Defines input parameters, in JSON format, to be passed on to job execution.

You must include the jobname and parameters elements.

{ 
    "jobName": "wordcount", 
    "parameters":
    { 
        "inputFile": "gs://dataflow-samples/shakespeare/kinglear.txt", 
        "output": "gs://controlmbucket/counts"
    } 
} 

(Optional) Defines the number of seconds to wait before checking the status of the job.

Default: 10

Determines the authorization credentials that are used to connect Control-M to GCP Dataproc.

Defines the project ID for your Google Cloud project.

Defines the Google Compute Engine region to create the job.

Defines one of the following Dataproc task types to execute:

• Workflow Template: Reusable workflow configuration that defines a graph of jobs with information on where to run those jobs.

• Job: A single Dataproc job.

(For a Workflow Template task type) Defines the ID of a Workflow Template.

(For a Job task type) Defines input parameters to be passed on to job execution, in JSON format.

You retrieve this JSON content from the GCP Dataproc UI, using the EQUIVALENT REST option in job settings.

(Optional) Defines the number of seconds to wait before checking the status of the job.

Default: 20

Determines the authorization credentials that are used to connect Control-M to Hadoop.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Variable Name: %%HDP-ACCOUNT

Determines the execution type for Hadoop job execution, as follows:

• DistCp Job

• Distributed Shell Job

• HDFS Commands Job

• HDFS File Watcher Job

• Impala Job

• Hive Job

• Java-Map-Reduce Job

• Oozie Job

• Pig Job

• Spark Job

• Sqoop Job

• Streaming Job

• Tajo Job

Variable Name: %%HDP-EXEC_TYPE

Defines the Pre commands performed before job execution (not for HDFS Commands jobs and Oozie Extractor jobs), and the argument for each command.

Determines whether the entire job fails if any of the Pre commands fail (not for HDFS Commands jobs and Oozie Extractor jobs).

Defines the Post commands performed before job execution (not for HDFS Commands jobs and Oozie Extractor jobs), and the argument for each command.

Defines the absolute destination path.

Variable Name: %%HDP-DISTCP_TARGET_PATH

Defines the source paths.

Variable Name: %%HDP-DISTCP_SOURCE_PATH-Nxxx_ARG

Defines the sets of attributes and values that are added to the command line.

Variable Names:

• Name: %%HDP-DISTCP_OPTION-Nxxx-NAME

• Value: %%HDP- DISTCP_OPTION-Nxxx-VAL

Determines what the Distributed Shell job runs, as follows:

• Command: Runs a shell command entry as defined by Command.

• Script File: Runs a script file as defined by Command, Script Full Path, and Shell Script Arguments.

Variable Name: %%HDP-SHELL_TYPE

Defines the shell command entry to run for the job execution.

Variable Name: %%HDP-SHELL_COMMAND

Defines the full path to the script file which is executed. The script file is located in the HDFS.

Variable Name: %%HDP-SHELL_SCRIPT_FULL_PATH

Defines the shell script arguments.

Variable Name: %%HDP-SHELL-Nxxx-ARG

Opens more attributes.

Defines the full path to the file or archive to upload as a dependency to the HDFS working directory.

Variable Names:

• Type: %%HDP-SHELL_FILE_DEP-Nxxx-TYPE

• Path: %%HDP-SHELL_FILE_DEP -Nxxx-PATH

Defines the additional option (Name and Value) to set when executing the job.

Variable Names:

• Name: %%HDP-SHELL_OPTION -Nxxx-NAME

• Value: %%HDP-SHELL_OPTION -Nxxx-VAL

Defines the environment variables for the shell script/command.

Variable Name: %%HDP-SHELL_ENV_VARIABLE-Nxxx-ARG

Defines the command for the argument to be performed with job execution.

Variable Name: %%HDP-HDFS_CMD_ACTION-Nxxx-CMD

Defines the full path of the file being watched.

Variable Name: %%HDP-HDFS_FILE_PATH

Determines the minimum file size in bytes to meet the criteria and finish the job as OK. If the file arrives, but the size is not met, the job continues to watch the file.

Variable Name: %%HDP-MIN_DETECTED_SIZE

Determines the maximum number of minutes to wait for the file to meet the watching criteria. If criteria are not met (file did not arrive, or minimum size was not reached) the job fails after this maximum number of minutes.

Variable Name: %%HDP-MAX_WAIT_TIME

Determines the source type to run the queries, as follows:

• Query File: Runs a query file as defined by Query File Full Path.

• Open Query: Runs an open query command as defined by Query.

Variable Name: %%HDP-IMPALA_QUERY_SOURCE

Defines the location of the file used to run the queries.

Variable Name: %%HDP-IMPALA_QUERY_FILE_PATH

Defines the query command used to run the queries.

Variable Name: %%HDP-IMPALA_OPEN_QUERY

Defines the full path to the Hive script on the Hadoop host.

Variable Name: %%HDP-HIVE_SCRIPT_NAME

Defines the list of parameters for the script.

Variable Names:

• Name: %%HDP-HIVE_SCRIPT_PARAM_Nxxx-NAME

• Value: %%HDP-HIVE_SCRIPT_PARAM-Nxxx-VAL

Defines the full path to the jar containing the Map Reduce Java program on the Hadoop host.

Variable Name: %%HDP-JAVA_JAR_NAME

Defines the class that is included in the jar containing a main function and the map reduce implementation.

Variable Name: %%HDP-JAVA_MAIN_CLASS

Defines the argument used by the command.

Variable Name: %%HDP-JAVA_Nxxx_ARG

Defines the job properties file path.

Variable Name: %%HDP-OOZIE_JOB_PROPERTIES_FILE

Defines the Oozie job properties.

A set of properties is comprised of the following:

• Key: Defines a key name associated with each property.

  Variable Name: %%HDP-OOZIE_PROPERTY-Nxxx-KEY

• Value: Defines a value associated with each property.

  Variable Name: %%HDP-OOZIE_PROPERTY-Nxxx-VAL

You can add new properties or override property values defined in the Job Properties File.

Defines the full path to the Pig program on the Hadoop host.

Variable Name: %%HDP-PIG_PROG_NAME

Defines the list of program parameters.

Determines whether to add Yarn aggregated logs to the job output.

Defines a list of properties (Name and Value) to be executed with the job.

These properties override the Hadoop defaults.

Defines the location of the Hadoop archives.

Determines the Spark program type, as follows:

• Python Script: As defined by Full Path to Script.

• Java / Scala Application: As defined by Application Jar File and Full Path to Script.

Variable Name: %%HDP-SPARK_PROG_TYPE

Defines the full path to the python script to execute.

Variable Name: %%HDP-SPARK_FULL_PATH_TO_PYTHON_SCRIPT

Defines the path to the jar including your application and all the dependencies.

Variable Name: %%HDP-SPARK_APP_JAR_FULL_PATH

Defines the main class of the application.

Variable Name: %%HDP-SPARK_MAIN_CLASS_TO_RUN

Defines the attribute arguments that are added at the end of the Spark command line either after the main class for Java / Scala Applications or after the script of the Python Script.

Variable Name: %%HDP-SPARK_Nxxx_ARG

Defines the sets of attributes and values that are added to the command line.

Variable Names:

• Name: %%HDP-SPARK_OPTION -Nxxx-NAME

• Value: %%HDP-SPARK_OPTION -Nxxx-VAL

Defines any valid Sqoop command necessary for job execution. Sqoop can only be used for job execution if defined in Sqoop connection attributes.

HDP-SQOOP_COMMAND

Determines whether to add Yarn aggregated logs to the job output.

Defines a list of properties (Name and Value) to be executed with the job.

These properties override the Hadoop defaults.

Defines the location of the Hadoop archives.

Defines the input file for the Mapper step.

Variable Name: %%HDP-INPUT_PATH

Defines the HDFS output path for the Reducer step.

Variable Name: %%HDP-OUTPUT_PATH

Defines the command that runs as a mapper.

Variable Name: %%HDP-MAPPER_COMMAND

Defines the command that runs as a reducer.

Variable Name: %%HDP-REDUCER_COMMAND

Defines the sets of attributes (Name and Value) that are added to the end of the Streaming command line.

Variable Names:

• Name: %%HDP-STREAMING_PARAM-Nxxx-NAME

• Value: %%HDP-STREAMING_PARAM-Nxxx-VAL

Defines the sets of attributes (Name and Value) that are added to the Streaming command line.

Variable Names:

• Name: %%HDP-GENERIC_PARAM-Nxxx-NAME

• Value: %%HDP-GENERIC_PARAM-Nxxx-VAL

Determines the source of the Tajo command, as follows:

• Input File: Runs the Tajo command from an input file as defined by the Full File Path.

  Variable Name: %%HDP-TAJO_INPUT_FILE

• Open Query: Runs an open query as the Tajo command, as defined by Open Query.

  Variable Name: %%HDP-TAJO_OPEN_QUERY

Defines the file path of the input file that runs the Tajo command.

Determines the authorization credentials that are used to connect Control-M to Snowflake.

Rules:

• Characters: 1−30

• Case Sensitive: Yes

• Invalid Characters: Blank spaces.

Determines the database that the job uses.

Determines the schema that the job uses.

A schema is an organizational model that describes the layout and definition of fields and tables, and their relationships to each other, in a database.

Determines one of the following Snowflake actions to perform:

• SQL Statement: Runs any number of Snowflake-supported SQL statements, such as queries, calling or creating procedures, database maintenance tasks, and creating and editing tables.

• Copy from Query: Copies a queried database and schema into an existing or new file in cloud storage.

• Copy from Table: Copies from an existing table.

• Create Table and Query: Creates a table, populated by a query, in the specified database and schema.

• Copy into Table: Copies data from a cloud storage location into an existing table in Snowflake.

• Start or Pause Snowpipe: Starts or pauses an existing Snowpipe.

• Stored Procedure: Calls an existing procedure and its arguments.

• Snowpipe Load Status: Monitors the status of a Snowpipe for a set period of time.

Determines one or more Snowflake-supported SQL commands.

Rule: Must be written in a single line, with strings separated by one space only.

Determines the maximum number of seconds to run the job in Snowflake.

Determines whether the following job-defining attributes are displayed:

• Parameters

• Role

• Bindings

• Warehouse

Defines Snowflake-provided parameters, in JSON format, that let you control how data is presented.

{ 
  "param1":"value1",
  "param2":"value2"
}

Determines the Snowflake role used for this Snowflake job.

A role is an entity that can be assigned privileges on secure objects. You can be assigned one or more roles from a limited selection.

Defines the values, in JSON format to bind to the variables used in the Snowflake job.

For more information on bindings, see the Snowflake documentation.

"1": 
    { 
      "type": "FIXED", 
      "value": "123" 
    } 
"2": 
    { 
      "type": "TEXT", 
      "value": "String" 
    }

Determines the warehouse used in the Snowflake job.

A warehouse is a cluster of virtual machines that processes a Snowflake job.

Determines whether to show a full JSON response in the log output.

Determines the number of seconds to wait before checking the status of the job.

Default: 20

Defines the cloud storage location.

Defines the query used for copying the data.

Defines the storage integration object, which stores an Identity and Access Management (IAM) entity and an optional set of blocked cloud storage locations.

Determines whether to overwrite an existing file in the cloud storage, as follows:

• Yes

• No

Determines one of the following file formats for the saved file:

• JSON

• CSV

Defines where the JSON or CSV file is saved.

You can save to Amazon Web Services, Google Cloud Platform, or Microsoft Azure.

Defines the name of the copied table.

Defines the name of the new or existing table where the data is queried.

Defines the query used for the copied data.

Defines the name of the Snowpipe.

A Snowpipe loads data from files when they are ready, or staged.

Defines the name of the table that the data is copied into.

Defines the cloud storage location from where the data is copied, in CSV or JSON format.

Determines whether to start or pause the Snowpipe, as follows:

• Start Snowpipe

• Pause Snowpipe

Defines the name of the stored procedure.

Defines the value of the argument in the stored procedure.

Defines the table that is monitored when loaded by the Snowpipe.

Defines the cloud storage location.

A stage is a pointer that indicates where data is stored, or staged.

Determines the number of days to monitor the Snowpipe load status.

Defines the cloud storage location where a CSV file log is created.

The CSV file log details the load status for each Snowpipe.