1. Lesson 1: Introduction to Trifacta Wrangler
Chapter 1C – Trifacta Interface Navigation

2. Lesson 1 – Chapter 1C Chapter 1C – TRIFACTA INTERFACE NAVIGATION
In this Chapter, you will learn important Trifacta terminology:
Transformer
Recipe
Data Quality Bar
Histogram
Data Type
Column Menus
Column Browser Panel
Column Details
A datasourse is a reference to a set of data that has been imported into the system.
This source is not modified within the application datasource and can be used in multiple datasets.
It is important to note that when you use Trifacta to wrangle a source, or file, the original file is not modified – therefore, it can be used over and over – to prepare output in multiple ways, for example.
Datasources are created in the Datasources Page, or when a new dataset is created.
There are two ways to add a datasource to your Trifacta instance:
You can locate and select a file in HDFS – HDFS stands for Hadoop File System. You can use the file browser to locate and select the file.
You can also upload a local file from your machine. Note that there is a 1 GB file size limit for local files.
Several file formats are supported:
CSV
LOG
JSON
AVRO
EXCEL – Note that if you upload an Excel file with multiple worksheets, each worksheet will be imported as a separate source.

3. Transformer
A datasourse is a reference to a set of data that has been imported into the system.
This source is not modified within the application datasource and can be used in multiple datasets.
It is important to note that when you use Trifacta to wrangle a source, or file, the original file is not modified – therefore, it can be used over and over – to prepare output in multiple ways, for example.
Datasources are created in the Datasources Page, or when a new dataset is created.
There are two ways to add a datasource to your Trifacta instance:
You can locate and select a file in HDFS – HDFS stands for Hadoop File System. You can use the file browser to locate and select the file.
You can also upload a local file from your machine. Note that there is a 1 GB file size limit for local files.
Several file formats are supported:
CSV
LOG
JSON
AVRO
EXCEL – Note that if you upload an Excel file with multiple worksheets, each worksheet will be imported as a separate source.

4. Transformer Identifies the data that you need to wrangle
Build recipes on samples
Recipe changes are immediately applied to your sample
Preview the results before you run it against the dataset at scale
A datasourse is a reference to a set of data that has been imported into the system.
This source is not modified within the application datasource and can be used in multiple datasets.
It is important to note that when you use Trifacta to wrangle a source, or file, the original file is not modified – therefore, it can be used over and over – to prepare output in multiple ways, for example.
Datasources are created in the Datasources Page, or when a new dataset is created.
There are two ways to add a datasource to your Trifacta instance:
You can locate and select a file in HDFS – HDFS stands for Hadoop File System. You can use the file browser to locate and select the file.
You can also upload a local file from your machine. Note that there is a 1 GB file size limit for local files.
Several file formats are supported:
CSV
LOG
JSON
AVRO
EXCEL – Note that if you upload an Excel file with multiple worksheets, each worksheet will be imported as a separate source.

5. Recipe
Review and modify the steps of the recipe that you have already created
Click the recipe panel to expand the recipe
Represented as a series of icons on the right side of the screen
You can navigate forward and backward in the the recipe to see the progression
Within the Transformer page, you build the steps of your script against a sample of the datase
A sample is typically a subset of the entire dataset. For smaller datasets, the sample may be the entire dataset. If you add or remove columns from your dataset, you can optionally generate a new sample for use in the Transformer page
As you build or modify your script, the results of each modification to the script are immediately reflected in the sampled data. So, you can rapidly iterate on the steps of your script within the same interface
When you work with data in the Trifacta Transformer, you are nearly always working with a sample of the data. This is not unusual in data processing, sampling is used to speed the iteration cycle.
The default Sample data that is loaded into the Transformer will be pulled from the beginning of the data file (starting with the first byte), until 500K, or end of the file – whichever comes first. 500K is the default setting.
A Random Sample is also collected from the Source. An initial random sample is created for datasets with pre-structured data, or for which some structure can be automatically inferred (via a splitrows transform). For such projects, you will see that the random sample is available for selection. The random sample should be roughly the same size (# of rows) as the first 500K.

6. Data Quality Bar Displays a bar that indicates:
Green: Values that match column’s datatype
Red: Values that do not match column’s datatype
Black: Missing values
Clicking on the data quality bar generates suggestions that apply to valid, invalid, or missing records
A datasourse is a reference to a set of data that has been imported into the system.
This source is not modified within the application datasource and can be used in multiple datasets.
It is important to note that when you use Trifacta to wrangle a source, or file, the original file is not modified – therefore, it can be used over and over – to prepare output in multiple ways, for example.
Datasources are created in the Datasources Page, or when a new dataset is created.
There are two ways to add a datasource to your Trifacta instance:
You can locate and select a file in HDFS – HDFS stands for Hadoop File System. You can use the file browser to locate and select the file.
You can also upload a local file from your machine. Note that there is a 1 GB file size limit for local files.
Several file formats are supported:
CSV
LOG
JSON
AVRO
EXCEL – Note that if you upload an Excel file with multiple worksheets, each worksheet will be imported as a separate source.

7. Histogram Displays a summary visualization of the data
Hover over the histogram to see values
Select one or more values in the histogram to prompt suggestion cards
Before you can start wrangling, or transforming, data, you must create a Dataset in Trifacta. You cannot wrangle data directly in a datasource! The dataset is your fundamental area of work within the Trifacta web application.
Datasets are created through the Workspace page. When you create the Dataset, you will assign a name and description.
A Dataset is the marriage of one or more datasources and a Wrangle script. Note that you will select a single source when you create a dataset, but additional sources can be added, using the Join or Union tools, for example.
A Wrangle script is a set of transformations to be executed against the datasource. We’ll talk more about the Wrangle language and generating scripts in a few minutes.
A includes a reference to a datasource, a Trifacta Web Application dataset script, and any number of executions using the script to transform the data in the datasource. See . Create Dataset Page A dataset may be associated with a project. A is a container for holding one or more datasets and their associated project results. See . Workspace Page A identifies the sequential set of steps that you define to cleanse and transform your data. script A script is specific to a dataset. Scripts are written in Wrangle, a . Scripts are interpreted by the domain-specific language for data transformation Trifacta and turned into commands that can be executed against the data.platform Scripts are created using the various visual tools in the . Transformer Page

8. Data Type To the left of the column header is the column’s datatype
A datasourse is a reference to a set of data that has been imported into the system.
This source is not modified within the application datasource and can be used in multiple datasets.
It is important to note that when you use Trifacta to wrangle a source, or file, the original file is not modified – therefore, it can be used over and over – to prepare output in multiple ways, for example.
Datasources are created in the Datasources Page, or when a new dataset is created.
There are two ways to add a datasource to your Trifacta instance:
You can locate and select a file in HDFS – HDFS stands for Hadoop File System. You can use the file browser to locate and select the file.
You can also upload a local file from your machine. Note that there is a 1 GB file size limit for local files.
Several file formats are supported:
CSV
LOG
JSON
AVRO
EXCEL – Note that if you upload an Excel file with multiple worksheets, each worksheet will be imported as a separate source.
To the left of the column header is the column’s datatype
Trifacta infers the column’s datatype

9. Column Menus To the right of the column header is the column menu
These menus allow for basic operations and also contextual transformations based on the columns datatype
Within the Transformer page, you build the steps of your script against a sample of the datase
A sample is typically a subset of the entire dataset. For smaller datasets, the sample may be the entire dataset. If you add or remove columns from your dataset, you can optionally generate a new sample for use in the Transformer page
As you build or modify your script, the results of each modification to the script are immediately reflected in the sampled data. So, you can rapidly iterate on the steps of your script within the same interface
When you work with data in the Trifacta Transformer, you are nearly always working with a sample of the data. This is not unusual in data processing, sampling is used to speed the iteration cycle.
The default Sample data that is loaded into the Transformer will be pulled from the beginning of the data file (starting with the first byte), until 500K, or end of the file – whichever comes first. 500K is the default setting.
A Random Sample is also collected from the Source. An initial random sample is created for datasets with pre-structured data, or for which some structure can be automatically inferred (via a splitrows transform). For such projects, you will see that the random sample is available for selection. The random sample should be roughly the same size (# of rows) as the first 500K.

10. Column Browser Panel
Open the Column Browser by click Columns in the toolbar of the Transformer page
Select one or more columns and perform actions on them
Toggle the display of individual columns in the Trifacta Application
Within the Transformer page, you build the steps of your script against a sample of the datase
A sample is typically a subset of the entire dataset. For smaller datasets, the sample may be the entire dataset. If you add or remove columns from your dataset, you can optionally generate a new sample for use in the Transformer page
As you build or modify your script, the results of each modification to the script are immediately reflected in the sampled data. So, you can rapidly iterate on the steps of your script within the same interface
When you work with data in the Trifacta Transformer, you are nearly always working with a sample of the data. This is not unusual in data processing, sampling is used to speed the iteration cycle.
The default Sample data that is loaded into the Transformer will be pulled from the beginning of the data file (starting with the first byte), until 500K, or end of the file – whichever comes first. 500K is the default setting.
A Random Sample is also collected from the Source. An initial random sample is created for datasets with pre-structured data, or for which some structure can be automatically inferred (via a splitrows transform). For such projects, you will see that the random sample is available for selection. The random sample should be roughly the same size (# of rows) as the first 500K.

11. Column Details
Provides a high-level overview of the column’s data including:
Data quality
Summary Statistics
Histograms
Frequency Charts
Within the Transformer page, you build the steps of your script against a sample of the datase
A sample is typically a subset of the entire dataset. For smaller datasets, the sample may be the entire dataset. If you add or remove columns from your dataset, you can optionally generate a new sample for use in the Transformer page
As you build or modify your script, the results of each modification to the script are immediately reflected in the sampled data. So, you can rapidly iterate on the steps of your script within the same interface
When you work with data in the Trifacta Transformer, you are nearly always working with a sample of the data. This is not unusual in data processing, sampling is used to speed the iteration cycle.
The default Sample data that is loaded into the Transformer will be pulled from the beginning of the data file (starting with the first byte), until 500K, or end of the file – whichever comes first. 500K is the default setting.
A Random Sample is also collected from the Source. An initial random sample is created for datasets with pre-structured data, or for which some structure can be automatically inferred (via a splitrows transform). For such projects, you will see that the random sample is available for selection. The random sample should be roughly the same size (# of rows) as the first 500K.