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Abort
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Aborts a transformation
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Aborting when there are too many errors (Chapter 7); also in Chapters 11 and 12
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Add constants
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Adds one or more constant fields to the stream
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Gathering progress and merging all together (Chapter 4); also in Chapters 7, 8, and 9
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Add sequence
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Gets the next value from a sequence
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Assigning tasks by Distributing (Chapter 4); also in Chapters 6 and 11
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Append streams
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Appends two streams in an ordered way
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Giving priority to Bouchard by using Append Stream (Chapter 4)
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Calculator
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Creates new fields by performing simple calculations
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Reviewing examination by using the Calculator step (Chapter 3); also in Chapters 6 and 8
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Combination lookup/update
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Updates a junk dimension. Alternatively, it can be used to update Type I SCD.
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Loading a region dimension with a Combination lookup/update step (Chapter 9); also in Chapter 12
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Copy rows to result
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Write rows to the executing job. The information will then be passed to the next entry in the job.
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Splitting the generation of top scores by copying and getting rows (Chapter 11)
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Data Validator
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Validates fields based on a set of rules
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Checking films file with the Data Validator (Chapter 7)
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Database join
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Executes a database query using stream values as parameters
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Using a Database join step to create a list of suggested products to buy (Chapter 9)
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Database lookup
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Looks up values in a database table
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Using a Database lookup step to create a list of products to buy (Chapter 9), also in Chapter 12
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Delay row
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For each incoming row, waits a given time before giving the row to the next step
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Generating custom files by executing a transformation for every input row (Chapter 11)
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Delete
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Delete data in a database table
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Deleting data about discontinued items (Chapter 8)
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Dimension lookup/update
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Updates or looks up a Type II SCD. Alternatively, it can be used to update Type I SCD or hybrid dimensions.
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Keeping a history of product changes with the Dimension lookup/update step (Chapter 9), also in Chapter 12
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Dummy (do nothing)
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This step type doesn't do anything! However it is used often.
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Creating a hello world transformation (Chapter 1), also in Chapters 2, 3, 7, and 9
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Excel Input
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Reads data from a Microsoft Excel (.xls ) file
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Browsing PDI new features by copying a dataset (Chapter 4); also in Chapter 8
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Excel Output
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Writes data to a Microsoft Excel (.xls ) file
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Getting data from an XML file with information about countries (Chapter 2); also in Chapters 4 and 10
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Filter rows
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Splits the stream in two upon a given condition. Alternatively, it is used to let pass just the rows that meet the condition.
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Counting frequent words by filtering (Chapter 3); also in Chapters 4, 6, 7, 9, 11, and 12
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Fixed file input
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Reads data from a fixed width file
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Calculating Scores with JavaScript (Chapter 5)
|
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Formula
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Creates new fields by using formulas. It uses Pentaho's libformula.
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Reviewing examination by using the Formula step (Chapter 3); also in Chapters 10 and 11
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Generate Rows
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Generates a number of equal rows
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Creating a hello world transformation (Chapter 1); also in Chapters 6, 9, and 10
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Get data from XML
|
Gets data from XML files
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Getting data from an XML file with information about countries(Chapter 2); also in chapters 3 and 9
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Get rows from result
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Reads rows from a previous entry in a job
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Splitting the generation of top scores by copying and getting rows (Chapter 11)
|
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Get System Info
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Gets information from the system like system date, arguments, etc.
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Updating a file with news about examination (Chapter 2) also in Chapters 7, 8, 10, 11, and 12
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Get Variables
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Takes the values of environment or Kettle variables and adds them as fields in the stream
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Creating the time dimension dataset(Chapter 6)
|
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Group by
|
Builds aggregates in a group by fashion. This works only on a sorted input. If the input is not sorted, only double consecutive rows are handled correctly
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Calculating World Cup statistics by grouping data (Chapter 3); also in Chapters 4, 7, and 9
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If field value is null
|
If a field is null, it changes its value to a constant. It can be applied to all fields of a same data type, or to particular fields
|
Enhancing a films file by converting rows to columns (Chapter 6)
|
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Insert / Update
|
Updates or inserts rows in a database table
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Inserting new products or updating existent ones (Chapter 8)
|
|
Mapping (sub-transformation)
|
Runs a subtransformation
|
Calculating the top scores with a subtransformation (Chapter 11)
|
|
Mapping input specification
|
Specifies the input interface of a sub-transformation
|
Calculating the top scores with a subtransformation (Chapter 11)
|
|
Mapping output specification
|
Specifies the output interface of a sub-transformation
|
Calculating the top scores with a subtransformation (Chapter 11)
|
|
Modified Java Script Value
|
Allows you to code Javascript to modify or create new fields. It's also possible to code Java
|
Calculating Scores with JavaScript(Chapter 5); also in Chapters 6, 7, and 11
|
|
Number range
|
Creates ranges based on a numeric field
|
Capturing errors while calculating the age of a film (Chapter 7); also in Chapter 8
|
|
Regex Evaluation
|
Evaluates a field with a regular expression
|
Validating Genres with a Regex Evaluation step (Chapter 7); also in Chapter 12
|
|
Row denormaliser
|
Denormalises rows by looking up key-value pairs
|
Enhancing a films file by converting rows to columns (Chapter 6)
|
|
Row Normaliser
|
Normalises data de-normalised
|
Enhancing the matches file by normalizing the dataset (Chapter 6)
|
|
Select values
|
Selects, reorders, or removes fields. Also allows you to change the metadata of fields
|
Reading all your files at a time using a single Text file input step (Chapter 2); also in Chapters 3, 4, 6, 7, 8, 9, 11, and 12
|
|
Set Variables
|
Sets Kettle variables based on a single input row
|
Updating a file with news about examinations by setting a variable with the name of the file (Chapter 11); also in Chapter 12
|
|
Sort rows
|
Sorts rows based upon field values, ascending or descending
|
Reviewing examinations by using the Calculator step (Chapter 3); also in Chapters 4, 6, 7, 8, 9, and 11
|
|
Split field to rows
|
Splits a single string field and creates a new row for each split term
|
Counting frequent words by filtering (Chapter 3)
|
|
Split Fields
|
Splits a single field into more than one
|
Calculating World Cup statistics by grouping data (Chapter 3); also in Chapters 6 and 11
|
|
Stream lookup
|
Looks up values coming from another stream in the transformation
|
Finding out which language people speak (Chapter 3); also in Chapter 6
|
|
Switch / Case
|
Switches a row to a certain target step based on the value of a field
|
Assigning tasks by filtering priorities with the Switch/ Case step (Chapter 4)
|
|
Table input
|
Reads data from a database table
|
Getting data about shipped orders (Chapter 8); also in Chapters 9, 10, and 12
|
|
Table output
|
Writes data to a database table
|
Loading a table with a list of manufacturers (Chapter 8), also in Chapters 9 and 12
|
|
Text file input
|
Reads data from a text file
|
Reading all your files at a time using a single Text file input step (Chapter 2); also in Chapters 3, 5, 6, 7, 8, and 11
|
|
Text file output
|
Writes data to a text file
|
Sending the results of matches to a plain file (Chapter 2); also in Chapters 3, 7, 9, 10, and 11
|
|
Update
|
Updates data in a database table
|
Loading a region dimension with a Combination lookup/update step (Chapter 9)
|
|
Value Mapper
|
Maps values of a certain field from one value to another
|
Browsing PDI new features by copying a dataset (Chapter 4)
|