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This example walks through the process of creating, identifying, and executing a job through automated methods. For this example, these tasks are accomplished using the following methods:

1. Locate or create flow. The datasets that you wrangle must be contained within a flow. You can add them to an existing flow or create a new one through the APIs.
2. Create dataset. Through the APIs, you create an imported dataset from an asset that is accessible through one of the established connections. Then, you create the recipe object through the API.
   1. For the recipe, you must retrieve the internal identifier.
   2. Through the application, you modify the recipe for the dataset.
3. Automate job execution. Using the APIs, you can automate execution of the wrangling of the dataset. 
   1. As needed, this job can be re-executed on a periodic basis or whenever the source files are updated.

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For more information on creating outputObjects, writeSettings, and publications, see API Workflow - Manage Outputs.

Step - Run Job

Through the APIs, you can specify and run a job. In the above example, you must run the job for the terminal dataset, which is POS-r01 in this case. This dataset contains references to all of the other datasets. When the job is run, the recipes for the other datasets are also applied to the terminal dataset, which ensures that the output reflects the proper integration of these other datasets into POS-r01. 

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{
  "wrangledDataset": {
    "id": 23
  },
  "overrides": {
    "execution": "photon",
    "profiler": true,
    "writesettings": [
      {
        "path": "hdfs://hadoop:50070/trifacta/queryResults/admin@example.com/POS-r01.csv",
        "action": "create",
        "format": "csv",
        "compression": "none",
        "header": false,
        "asSingleFile": false
      }
    ]
  },
  "ranfrom": null}

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