Terminology applicable to Dataprep by Trifacta®.
NOTE: This list is not comprehensive.
Object Model Terms
These terms apply to the objects that you import, create, and generate in
Dataprep by Trifacta.
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.
A container for holding a set of related imported datasets, recipes, and output objects. Flows are managed in Flow View page.
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.
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.
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.
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.
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.
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.
Optionally, you can create a profile of your generated results. This profile is available through the Trifacta application and may assist in analyzing or troubleshooting issues with your dataset. See Overview of Visual Profiling.
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.
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.
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.
- See also trigger and scheduled destination.
- See Overview of Automator.
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.
- See also schedule and trigger.
- See Overview of Automator.
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 RapidTarget.
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 Automator.
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.
These terms apply to the Trifacta application, a web-based application for interacting with your datasets, flows, and recipes.
Column Browser panel
Browse columns of your dataset, select and perform operations on one or more selected columns. See Column Browser Panel.
Column Details panel
Examine details and profile of the data in the selected column. See Column Details Panel.
Perform transformation operations on the selected column from a list of menu options, including changing the column data type. See Column Menus.
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.
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.
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.
Landing page after login. See Home Page.
Import Data page
Import data from a valid connection as an imported dataset. See Import Data Page.
Manage your imported datasets and reference objects. See Library Page.
Review the list of jobs that you have launched. View status, explore job details, and export results. See Jobs Page.
Job Details page
Review the details of your job, including an optional profile of the resulting data. See Job Details Page.
Feature that enables matching of columns and data types of your dataset with a pre-defined target schema.
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.
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.
Search for transformations to build as the next step in your recipe. See Search Panel.
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.
Select from common transformations in a toolbar across the top of the data grid. See Transformer Toolbar.
Review and customize transformation steps. See Transform Builder.
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.
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.
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 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.
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.
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.
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.
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.
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.
|Logical Operators||and, or, and not operators|
|Numeric Operators||Add, subtract, multiply, and divide|
|Comparison Operators||Compare two values with greater than, equals, not equals, and less than operators|
|Ternary Operators||Use 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.
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.
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.
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 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:
- See STRINGGREATERTHAN Function.
- See STRINGGREATERTHANEQUAL Function.
- See STRINGLESSTHAN Function.
- See STRINGLESSTHANEQUAL Function.
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 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.
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 Trifacta.
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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