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Terminology applicable to  Dataprep by Trifacta.

NOTE: This list is not comprehensive.


Object Terms

These terms apply to the objects that you import, create, and generate in Dataprep by Trifacta.


In an application role, the author privilege allows the highest level of access, except for ownership, to application objects. This privilege can be applied to object types within an assignable role. See Overview of Authorization.


Anyone who has been provided editor- or author-level access to an object. See Overview of Sharing.

data quality rule

A data quality rule is a pass/fail test of your data against a condition that you define. Data quality rules can be created to validate your data against the meaning of the data and to assess your efforts to transform it. For more information, see Data Quality Rules Panel.

dataset with parameters

An imported dataset that has been created with parameterized references, typically used to collect multiple assets stored in similar locations or filenames containing identical structures. For example, if you stored orders in individual files for each week in a single directory, you could create a dataset with parameters to capture all of those files in a single object, even if more files are added at a later time.

The path to the asset or assets is specified with one or more of the following types of parameters: Datetime,  Pattern , regular expression, wildcard, or variable.

data type

A data type refers to the expected class of values for a column of data. A data type defines the types of information that are expected and can include specific formatting of that information. Column values that do not meet the expectations of the column data type are determined to be invalid for the data type. 


In an application role, the editor privilege allows viewing and modifying application objects. This privilege can be applied to object types within an assignable role. See Overview of Authorization.


A container for holding a set of related imported datasets, recipes, and output objects. Flows are managed in Flow View page.

flow parameter

A named reference that you can apply in your recipe steps. When applied, the flow parameter is replaced with its corresponding value, which may be the default value or an override value. See Overview of Parameterization.

imported dataset

A reference to an object that contains data to be wrangled in  Dataprep by Trifacta. An imported dataset is created when you specify the file(s) or table(s) that you wish to read through a connection. 


A job is the sequence of processing steps that apply each step of your recipe in sequence across the entire dataset to generate the desired set of results.


A macro is a sequence of one or more reusable recipe steps. Macros can be configured to accept parameterized inputs, so that their functionality can be tailored to the recipe in which they are referenced.


Associated with a recipe, an output is a user-defined set of files or tables, formats, and locations where results are written after a job run on the recipe has completed.  

output destinations

An output may contain one or more destinations, each of which defines a file type, filename, and location where the results of the output are written.

output parameter

You can create variable or timestamp parameters that can be applied to parts of the file or table paths of your outputs. Variable values can be specified at the time of job execution.


A plan is a sequence of triggers and tasks that can be applied across multiple flows. For example, you can schedule plans to execute sequences of flows at a specified frequency. For more information, see Overview of Operationalization.


Feature Availability: This feature may not be available in all product editions. For more information on available features, see Compare Editions.

A privilege determines the level of access to a type of Dataprep by Trifacta object. Privileges are assigned using roles. For more information, see Overview of Authorization.

parameter override

A value that is applied instead of the default or inherited value for a parameter. A parameter override may be applied at the flow level or at the time of job execution.


A sequence of steps that transforms one or more datasets into a desired output. Recipes are built in the Transformer page using a sample of the dataset or datasets. When a job is executed, the steps of the recipe are applied in the listed order to the imported dataset or datasets to generate the output.


A pointer to the output of a recipe. A reference can be used in other flows, so that those flows get the latest version of the output from the referenced recipe.

reference dataset

A reference that has been imported into another flow.


A set of generated files or tables containing the results of processing a selected recipe, its datasets, and all upstream dependencies. See Job Details Page.

results profile

Optionally, you can create a profile of your generated results. This profile is available through the Dataprep by Trifacta application and may assist in analyzing or troubleshooting issues with your dataset. See Overview of Visual Profiling.


Feature Availability: This feature may not be available in all product editions. For more information on available features, see Compare Editions.

A role is a set of privileges that governs access levels to one or more types of objects. For more information, see Overview of Authorization.


When you review and interact with your data in the data grid, you are seeing the current state of your recipe applied to a sample of the dataset. If the entire dataset is smaller than the defined limit, you are interacting with the entire dataset.

You can create new samples using one of several supported sampling techniques. See Overview of Sampling.


You can associate a single schedule with a flow. A schedule is a combination of one or more trigger times and the one or more scheduled destinations that are generated when the trigger is hit. A schedule must have at least one trigger and at least one scheduled destination in order to work.

scheduled destination

When a schedule's trigger is fired, each recipe that has a scheduled destination associated with it is queued for execution. When the job completes, the outputs specified in the scheduled destination are generated. A recipe may have only one scheduled destination, and a scheduled destination may have multiple outputs (publishing actions) associated with it.


A schema defines the column names, data types, and ordering of your dataset. Schemas apply to relational datasources and some file types, such as Avro or Parquet. For more information, see Overview of Schema Management


When a plan is triggered, a snapshot of all tasks in the plan is taken. The tasks of the plan are executed against this snapshot. Subsequent revisions to these objects may impact the execution of the plan. For more information, see Overview of Operationalization.


A set of columns, their order, and their formats to which you are attempting to wrangle your dataset. A target represents the schema to which you are attempting to wrangle. You can assign a target to your recipe, and the schema can be superimposed on the columns in the data grid, allowing you to make simple selections to transform your dataset to match the column names, order, and formats of the target. See Overview of Target Schema Mapping.


A task is an executable action that is part of a plan. For example, when a plan is triggered, the first task in the plan is queued for execution, which may be to execute all of the recipes and their dependencies in a flow. For more information, see Overview of Operationalization.


A trigger is a periodic time associated with a schedule. When a trigger's time occurs, all of flows associated with the trigger are queued for execution.

  • A schedule can have multiple triggers. See also schedule and scheduled destination.
  • For more information on flow-based triggers, see Overview of Scheduling.

variable (dataset)

A replacement for the parts of a file path to data that change with each refresh. A variable can be overwritten as needed at job runtime.


In an application role, the viewer privilege allows read-only access application objects. This privilege can be applied to object types within an assignable role. See Overview of Authorization.

Application Terms

These terms apply to the Dataprep by Trifacta application, a web-based application for interacting with your datasets, flows, and recipes.


The main panel of Flow View where you can add, arrange, and remove flow objects. See Flow View Page.Plan View also contains a canvas area. See Plan View Page.

Cluster Clean

Standardize values in a column by clustering similar values together. See Overview of Cluster Clean

Column By Example

Creates a new column of data by providing example values from an existing column. See Overview of TBE.

Column Browser panel

Review sampled data across multiple columns through the column browser. You can also use the Column Browser panel to toggle the display of individual columns. See Column Browser Panel.

Column Details panel

Examine details and profile of the data of a selected column. See Column Details Panel.

Column menu

Perform transformation operations on the selected column from a list of menu options, including changing the column data type. See Column Menus.

Column histogram

At the top of the column, review the counts of values in the column. Select one or more values in the column through the histogram. See Column Histograms.

Connections page

Create or edit connections to external storage. See Connections Page.

Data Grid

In the Transformer page, the data grid displays a sample of the dataset at the currently selected step in the recipe. Make selections in the dataset to prompt suggestions for transformations to add to your recipe. See Data Grid Panel.

Data Quality bars

Review color-coded counts of valid, missing, and mismatched values in your column based on the column's data type. Select a color bar to be prompted with suggestions for transformations on the relevant rows. See Data Quality Bars.

Data Quality Rules panel

In the Data Quality Rules panel, you can build pass/fail rules to test the quality of your data. These rules can be used to assess general data quality or to test the applicability of the values to your use case. For more information, see Data Quality Rules Panel.

Data Type menu

Change the data type for the column from the icon to the left of the column header. See Column Menus.

Dataset Details page

Examine details about your dataset, including source of data and other information. See Dataset Details Page.

email notifications

By default, the Dataprep by Trifacta application sends notifications to users on the success or failure of their jobs and plans. The delivery of these notifications can be disabled as needed. See Email Notifications Page.

Flag for Review

The Flag for review feature enables flow users to flag recipe steps for others to review, provide inputs, and sign off on the changes before jobs are permitted to execute. See Flag for Review.

Flows page

Create, manage, and export your flows. See Flows Page.

Flow View page

Build your flow objects, including recipes, outputs, and references. See Flow View Page.

Home page

Landing page after login. See Home Page.

Import Data page

Import data from a valid connection as an imported dataset. See Import Data Page.

Library for Data page

Manage your imported datasets and reference objects. See Library for Data Page.

Job Details page

Review the details of your job, including an optional profile of the resulting data. See Job Details Page.

Job History page

Review the list of jobs that you have launched. View status, explore job details, and export results. See Job History Page.

Job monitoring

Job monitoring enables users of the Dataprep by Trifacta application to monitor job progress through each phase of its execution. See Overview of Job Monitoring.

Plan View

Plan View page enables you to build, arrange, and execute a sequence of one or more tasks in an orchestrated plan. Plans can be scheduled for execution or run on an ad-hoc basis. See Plan View Page.

Product edition

A product edition is a set of features and limits on resources applied to your workspace. Depending your account, the same product edition can be applied across multiple workspaces. See Product Editions.

Recipe panel

Add, edit, and remove steps from your current recipe. Apply changes and see updates immediately in the data grid sample.

Run Job page

Configure job, visual profiling, and job outputs before launching. See Run Job Page.

Samples panel

Review, create, and delete samples for the current recipe.

Sample Jobs page

Review status of all samples that you have initiated. Administrators can access the samples of all users. For more information, see Sample Jobs Page.


Feature that enables automated execution of flows according to user-defined schedules. See Overview of Scheduling.

Search panel

Search for transformations to build as the next step in your recipe. See Search Panel.

Settings page

Review and modify settings. See Preferences Page.

Selection Details panel

Based on selections you make in the data grid, you can review profiling information and a set of suggested transformations to add to your recipe. See Selection Details Panel.

Target schema mapping

Feature that enables matching of columns and data types of your dataset with a pre-defined target schema. 

Transformer toolbar

Select from common transformations in a toolbar across the top of the data grid. See Transformer Toolbar.

Transform Builder

Review and customize transformation steps. See Transform Builder.

Transformer page

Review sampled data, explore suggestions and previews, and build transformation steps. See Transformer Page.

User Profile page

Review and modify settings applicable to your user account. See User Profile Page.

Visible Columns panel

Review and toggle the visibility of the columns in your dataset. See Visible Columns Panel.



Orchestration refers the the sequencing, execution, and monitoring of a series of tasks in the platform. In the Dataprep by Trifacta Cloud, orchestration is defined using plans. See Overview of Operationalization.

sample checkpointing

As you build more complex recipes and flows, it's a good idea to create samples periodically in your recipe steps. All steps between the currently displayed sample and the currently displayed recipe step are executed in the browser, so this type of checkpointing with samples can improve performance. For more information on best practices in sampling, see Overview of Sampling

schema drift

Feature that detects changes to the schema of your imported datasets before or during job execution. See Overview of Schema Management.

schema refresh

Feature that refreshes the schema of your imported datasets within the Dataprep by Trifacta based on changes to the schemas of their datasources. See Overview of Schema Management.

type system

The system within the Dataprep by Trifacta Cloud for managing data types.

  • The Dataprep by Trifacta Cloud can read data types from a variety of source systems. These types are then mapped to internal Dataprep by Trifacta data types
  • During recipe development, data types may be re-inferred by the Dataprep by Trifacta Cloud, as the data within your columns changes.  
  • During job execution and publishing to a target system, Dataprep by Trifacta data types may be mapped to a different set of data types, depending on the target.

Recipe Development Terms

These terms pertain to building recipes in Wrangle in the Transformer page.


An input to a function. See Wrangle Language.


Several functions can be used to group values in a column into bins, which can assist in preparing your data for downstream use. See Prepare Data for Machine Processing.

data type

A data type is the set of constraints on expected values in a column. When you specify the data type for a column, you provide a means for the platform to identify the values in the column that do not match the selected type, which assists in wrangling the mismatched values. See Supported Data Types.

Data types can be selected from the column menus. See Column Menus.


An input to a recipe that is not the primary datasource for the recipe. For example, if your recipe includes a join step, the dataset that is joined into your recipe is an upstream dependency. Recipe steps and changes outside of the Dataprep by Trifacta application can create dependency errors, in which an upstream object can no longer be found and the reference to it cannot be resolved. These issues must be fixed prior to successful execution of a job. For more information, see Fix Dependency Issues

file encoding

A file's encoding defines the set of characters that are in use in the file. There are many different encoding systems in use around the world. To represent English language, which uses a 26-character alphabet, UTF-8 is sufficient. However, to represent Asian character sets, which may contain thousands of characters, a different and broader set of characters is required. See Supported File Encoding Types.

When a file is imported,  Dataprep by Trifacta assumes that the file is in the default encoding type. As needed, you can change the encoding type that is used to import the file. See Change File Encoding.

full scan

A full scan sample is generated across the entire dataset on default running environment. Full scan samples are more representative of the total dataset. However, they can take a while to generate. For more information, see Samples Panel.


A function in Wrangle is an action that is applied to a set of values as part of a transformation step. A function can take 0 or more parameters as inputs, yielding a single output of a specific data type. For a list of supported functions, see Language Index

initial structure

When a file-based dataset is imported,  Dataprep by Trifacta attempts to detect the format and structure of the data and then to apply a set of initial parsing steps to transform the data for display in tabular form in the data grid. These steps may vary depending on the file format. See Initial Parsing Steps.

These steps do not appear in the recipe. As needed, you can disable the detection of structure on import. When disabled, these steps are added as the first steps of the recipe, where you can edit or remove them as needed. See Remove Initial Structure.


This database concept can be applied to datasets. In a join, two datasets are merged into one, based on a set of key columns. Values in these columns that match across the datasets are used to determined the values from each dataset to include in the joined dataset. See Join Types.

Joins are created as steps in your recipe. See Join Window.


A retrieval of a row of values from another dataset based on common values in columns in each dataset. A lookup is useful for bringing in reference information based on values in one of the columns of your dataset. See Lookup Wizard.


Values in a column that do not conform to range or format of expected values for the column's data type.  


Cell values in the dataset that are empty.

multi-dataset operation

A multi-dataset (MDS) operation refers to any step in your recipe that uses two or more datasets. Joins and unions are examples of multi-dataset operations.

nested expression

An expression that is inside another expression. Example:


Dataprep by Trifacta supports the use of nested expressions in your recipe steps. See Wrangle Language.


A value that does not exist in the dataset. See Manage Null Values.


A single character that represents an arithmetic function or comparison. For example, the Plus sign (+) represents the add function. 

Operator CategoryDescription
Logical Operatorsand, or, and not operators
Numeric OperatorsAdd, subtract, multiply, and divide
Comparison OperatorsCompare two values with greater than, equals, not equals, and less than operators
Ternary OperatorsUse ternary operators to create if/then/else logic in your transforms.


In statistics, an outlier refers to a value that is unusually above or below from the mean. In  Dataprep by Trifacta, an outlier is 4 standard deviations away from the mean. 

You can review outliers for column values. See Column Statistics Reference.

parameter (language)

An input to a transform in  Wrangle. See Wrangle Language.


In  Dataprep by Trifacta, a pattern is an object that describes a sub-string within a value. Patterns can be described using regular expressions, a common standard, or  Patterns , a proprietary simplification of regular expressions. See Text Matching.

Patterns are widely used in the product for identifying and extract values from data, data type validation, and supporting pattern-based suggestions.

  • See  Pattern .
  • See regular expression.

plan metadata reference

A plan metadata reference is a programmatic reference to some aspect of a plan, its tasks, or results of the execution. These metadata references can be inserted into the requests and responses of tasks in the plan for delivery to other systems. For more information, see Plan Metadata References

quick scan

A quick scan sample is generated using an appropriate selection of rows from the dataset. Since these samples are generated in Trifacta Photon, they are faster to produce. For more information, see Samples Panel.

range join

A range join is a type of join in which key values may be matched with a range of values in the joined-in dataset. For example, you can create a range join based on the source key value being greater than values in the key column of the joined-in dataset. A range join can explode the size of your resulting dataset. For more information, see Configure Range Join

Joins are created as steps in your recipe. See Join Window.

regular expression

Regular expressions are a powerful yet complex method of describing patterns of values for matching purposes. See Text Matching.

source row number

The row number for a record as it appeared in the original dataset. Source row number information can be obtained by function. This function may return a null value if multi-dataset operations, such as union and join, have been performed on the dataset. See SOURCEROWNUMBER Function

source metadata reference

A source metadata reference is a programmatic reference to some aspect of the source file for your dataset. Using these programmatic references, you can write source information for your original datasource into your dataset for future reference. For more information, see Source Metadata References


Dataprep by Trifacta provides multiple mechanisms to standardize column values using patterns, clustering algorithms, or functions. See Overview of Standardization.

string collation

String collation refers to a method of comparison of strings based on a set of rules.  Dataprep by Trifacta includes the following functions to perform string collation-based comparisons:


A transformation is the unit of action in a recipe step. A transformation applies one or more actions on a set of rows or columns. Transformations are specified in the Transformer page through the Transform Builder. See Transform Builder.

For a list of available transformations, see Transformation Reference.


A transform in  Wrangle is an action that is applied to rows or columns of your dataset. A transform can take zero or more parameters as inputs. A parameter may contain a reference to a column, a literal value, or a function.

NOTE: Transforms are not available through the Dataprep by Trifacta application. Instead, you build transformations, which are more complex steps that reference transforms from the underlying language.

For a list of supported transforms, see Language Index.

Dataprep by Trifacta pattern

A simplification of regular expressions,  Patterns  are custom selectors for patterns in your data and provide a simpler and more readable alternative to regular expressions. See Text Matching.


A union combines two or more datasets such that the rows of the second and later datasets are appended to the end of the first dataset. In a union operation, the columns must be matched up, or the results are a ragged dataset. 

Unions are created as steps in your recipe. See Union Page.


An informal term for the process of data preparation. Data wrangling was invented by the co-founders of Alteryx Inc


Cloud SQL

A fully managed service that enables you to set up, manage, and monitor relational databases.

Connect string options

When you create a connection to a datastore, you may be able to specify a set of one or more options, which are appended to the connection string passed to the datastore for access. Connect string options are specified as part of the definition of each connection object within the Dataprep by Trifacta application. See Create Connection Window.

Early Preview

Provide early access (read-only) to use the new connection types to work with the data in your connected datastores. See Early Preview Connection Types.

Long loading

Long loading refers to the process by which large datasets can be asynchronously loaded into Dataprep by Trifacta application. From relational sources or sources that require conversion, larger datasets can be queued for loading and conversion for use. While these datasets are being loaded, you can continue to use the Dataprep by Trifacta application for other tasks.

OAuth 2.0

This industry-standard framework enables Dataprep by Trifacta application to connect to third-party datastores using an app-and-client model. See Enable OAuth 2.0 Authentication.

SSH tunneling

For some relational connections, you can configure the Dataprep by Trifacta application to use SSH tunneling for some connection types. See Configure SSH Tunnel Connectivity.

Admin Terms

These terms apply to administration of your project or workspace and the underlying platform.

Dataprep Project Settings page

A page in the Dataprep by Trifacta application for managing a project's features and other configuration options within Dataprep by Trifacta. See Dataprep Project Settings Page.


A Google Cloud Platform concept, a project is a logical structure for organizing a set of users, services, and their data within the Google Cloud Platform. Dataprep by Trifacta is a Google Cloud Platform service that is provisioned to a project.

Miscellaneous Terms

Epoch/Unix time

Unix time (a.k.a. POSIX time or Epoch time) is a system for describing instants in time, defined as the number of seconds that have elapsed since 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970, not counting leap seconds.

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