• What is predictive transformation? You see an issue in your sampled data. Whether it is part of a value, multiple values in a column, or the entire column itself, you select it. Immediately, the platform surfaces a set of suggestions for you.
• What is a recipe? A recipe is a sequence of transformation steps that you create to transform your source dataset.
• What is a job? A job executes your set of recipe steps on the source data, without modifying the source, for delivery to a specified output, which defines location, format, compression, and other settings.
• What is a flow? Datasets, recipes, and outputs can be grouped together into objects called flows. A flow is a unit of organization in the platform.
• What is an imported dataset? An imported dataset is simply a reference to the original data; the data does not exist within the platform. An imported dataset can be a reference to a file, multiple files, database table, or other types of data.
• What is output? Outputs contain one or more publishing destinations, which define the output format, location, and other publishing options that are applied to the results generated from a job run on the recipe.
• What is a reference? References allow you to create a reference to the output of the recipe's steps in another dataset.
• What are macros? As needed, you can create reusable sequences of steps that can be parameterized for use in other recipes.
• What is a connection? A connection is a configuration object that provides a personal or global integration to an external datastore. Reading data from remote sources and writing results are managed through connections.
• What is a schedule? You can associate a schedule with a flow. A schedule is a combination of one or more triggers and the outputs that are generated from them.
• What is a plan? A plan is a sequence of triggers and tasks that can be executed across multiple flows. A plan is executed on a snapshot of all objects at the time that the plan is triggered.
• What is a task? A task is an executable action that is taken as part of a plan's sequence. For example, task #1 could be to execute a flow that imports all of your source data. Task #2 executes the flow that cleans and combines that data.
• What is a target? a target is the set of columns, their order, and the formats to which you are attempting to wrangle your dataset.
• On what level or object can you set parameters? Environment, Dataset, Flow, Output
• What types of parameters can you apply to datasets? Datetimes, variables, and pattern parameters
• What types of parameters can you apply to flows? Literal values, patterns, and regular expressions
• What parameter types can you apply to outputs? Datetimes, variables
• What happens to upstream flow parameters? If your flow references a recipe or dataset that is sourced from an upstream flow, the flow parameters from that flow are available in your current flow. That value of the parameter at the time of execution is passed to the current flow.
• In what order are parameters evaluated? Run-time > Flow > Default values > Upstream