Workflow Operators

Operators are the building blocks of Team Studio workflows. Each one represents a unit of computation or a data source. Operators can perform tasks such as data loading and transformation or complete more advanced tasks such as training and evaluating machine learning models.

The following topics describe the most common operator concepts.

  • Operator Actions
    You can manipulate the operators and the connections between them in the workflow editor. Also, each operator has properties that you can set and edit in its dialog box.
  • Data Management
    With Team Studio data operators, you can manage multiple data sets or files. You can also move data between databases or from a Hadoop data source to a database, or from a database to Hadoop data source. Team Studio data operators provide the means to manage your data.
  • Data Exploration
    You can explore data using one of the visual operators in the Exploration category.
  • Data Transformation
    Team Studio provides operators to clean and prepare data for use before modeling.
  • Data Modeling and Model Validation
    Team Studio provides a robust set of modeling operators and operators that provide validation for models.
  • Prediction
    Prediction operators follow Modeling operators. Each modeling algorithm operator must be assembled with an input data source for analysis and a Prediction operator for running the model against a new source in order to return a predicted dataset.
  • Spark Node Fusion
    You can use Spark Node Fusion to have multiple operators in a single Spark job (also called "Spark context"). This allows the job to run faster because it is not recreating a new job and persisting results to HDFS at each analytical step.
  • Specialized Tools
    Team Studio provides specialized sets of operators for such areas as natural language processing (NLP) and financial fields.
  • Workflow Operator Reference
    The Team Studio workflow operators are categorized by specific function or type.
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