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: 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, which define the variables, for your AWS Data Pipeline in JSON format.

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: 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: 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.

When there are no parameters, specify the following value:

"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: 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 to override when the job runs, defined in JSON format as pairs of name and value, as follows::

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

For no parameters, specify {}.

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

Rules:

• Characters: 1−30

• Case sensitive: Yes

• Invalid characters: Spaces

Determines the ID of the 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 through the Azure Databricks documentation.

When there are no parameters, specify the following value:

"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 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 to be passed on to job execution, in JSON format (name:value pairs).

This JSON must include the jobname and parameters elements, as in the following example:

{ 

    "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: 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 one of the following types of authorization credentials, which are used to connect Control-M to Snowflake:

• Snowflake

• Snowflake IdP

Rules:

• Characters: 1−30

• Case sensitive: Yes

• Invalid characters: 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.

• Create Snowpipe: Creates a Snowpipe and saves it to a file in cloud storage.

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

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.

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 table that the data is copied into.

Defines the stage from where the data is copied.

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.