Preface
Transformations
- Active and passive transformations
- Transformation types
- Transformations in advanced mode
- Licensed transformations
- Incoming fields
  - Field name conflicts
    - Creating a field name conflict resolution
  - Field rules
- Data object preview
- Variable fields
- Transformation caches
- Expression macros
- File lists
- Multibyte hierarchical data
Source transformation
- Source object
- File sources
- Database sources
- Web service sources
- Partitions
  - Partitioning rules and guidelines
  - Partitioning examples
- Reading hierarchical data in advanced mode
- Multibyte hierarchical data
- Source fields
  - Editing native data types in complex file sources
  - Editing transformation data types
Target transformation
- Target object
  - Target file creation on advanced clusters
- File targets
- Database targets
- Web service targets
  - Web service operations for targets
  - Field mapping for web service targets
- Partitions
- Writing hierarchical data in advanced mode
- Multibyte hierarchical data
- Target fields
- Target transformation field mappings
- Configuring a Target transformation
Aggregator transformation
- Group by fields
- Sorted data
- Aggregate fields
- Advanced properties
- Hierarchical data in advanced mode
- Aggregator transformation example
Cleanse transformation
- Cleanse transformation configuration
  - Cleanse asset considerations
  - Synchronizing data quality assets
- Cleanse transformation field mappings
- Cleanse transformation output fields
- Advanced properties
Data Masking transformation
- Masking techniques
- Configuration properties for masking techniques
- Credit card masking
- Email masking
  - Advanced email masking
- IP address masking
- Key masking
- Phone number masking
- Random masking
- Social Insurance number masking
- Social Security number masking
- Custom substitution masking
- Dependent masking
  - Dependent masking parameters
- Substitution masking
- URL address masking
- Mask rule parameter
- Creating a Data Masking transformation
- Consistent masked output
  - Rules and guidelines
  - Example
- Data Masking transformation example
Data Services transformation
- Dynamic service name
- Status tracing messages
- Data Services properties
- Data Services transformation input fields
- Data Services transformation output fields
- Data Services transformation field mapping
Deduplicate transformation
- Deduplication and consolidation operations
- Identity population data
- Groups in duplicate analysis
- Deduplicate transformation configuration
- Deduplicate transformation field mappings
- Metadata fields on the Deduplicate transformation
- Link scores and driver scores
- Deduplicate transformation output fields
- Advanced properties
Expression transformation
- Expression fields
- Expression editor
- Transformation language components for expressions
- Expression syntax
- String and numeric literals
- Adding comments to expressions
- Reserved words
- Window functions
  - Frame
  - Partition and order keys
- Example: Use a window to calculate expiration dates
- Example: Use a window to flag GPS pings
- Example: Run an aggregate function on a window
- Advanced properties
- Hierarchical data in advanced mode
Filter transformation
- Filter conditions
- Advanced properties
- Hierarchical data in advanced mode
Hierarchy Builder transformation
- Using a Hierarchy Builder transformation
- Sample or schema
- Hierarchical schemas
  - Rules and guidelines for hierarchical schemas
  - Creating a hierarchical schema
- Intelligent structure models
- Output settings
- Field mapping
  - Selecting a primary or foreign key
  - Selecting the fields to map
- Advanced properties
- Multibyte hierarchical data
- Hierarchy Builder transformation example
Hierarchy Parser transformation
- Using a Hierarchy Parser transformation
- Hierarchy Parser rules and guidelines
- Sample or schema
- Hierarchical schemas
  - Rules and guidelines for hierarchical schemas
  - Creating a hierarchical schema
- Input settings
  - Selecting a hierarchical schema
  - Creating a hierarchical schema from sample
- Input field selection
- Field mapping
  - Selecting the elements to convert
- Output fields
- Selecting an output group
- Multibyte hierarchical data
- Hierarchy Parser transformation example
Hierarchy Processor transformation
- Hierarchy Processor transformation overview
- Processing relational output
- Processing hierarchical output
- Processing flattened output
Input transformation
- Input fields
Java transformation
- Defining a Java transformation
- Classpath configuration
- Java transformation fields
- Configuring Java transformation properties
- Developing the Java code
- Compiling the code
  - Viewing the full class code
- Troubleshooting a Java transformation
  - Finding the source of compilation errors
  - Identifying the error type
- Java transformation example
Java transformation API reference
- failSession
- generateRow
- getInRowType
- incrementErrorCount
- invokeJExpression
- isNull
- logError
- logInfo
- setNull
- setOutRowType
Joiner transformation
- Join condition
- Join type
- Advanced properties
- Hierarchical data in advanced mode
- Creating a Joiner transformation
Labeler transformation
- Labeler transformation configuration
- Labeler transformation field mappings
- Labeler transformation output fields
Lookup transformation
- Lookup object
  - Lookup object properties
    - Multiple match policy restrictions
  - Custom queries
- Lookup condition
- Lookup return fields
- Advanced properties
- Lookup SQL overrides
  - Guidelines for overriding the lookup query
- Lookup source filter
- Dynamic lookup cache
- Persistent lookup cache
  - Rebuilding the lookup cache
- Unconnected lookups
  - Configuring an unconnected Lookup transformation
  - Calling an unconnected lookup from another transformation
- Connected Lookup example
- Dynamic Lookup example
- Unconnected Lookup example
Machine Learning transformation
- Deploying the model as a REST endpoint
- Accessing the machine learning model
- Mapping fields to the request schema
  - Mapping hierarchical fields
  - Request mapping options
- Viewing response fields
- Configuring bulk requests
  - Bulk request options
- Configuring an API proxy
- Troubleshooting
- Error handling
Mapplet transformation
- Mapplet transformation configuration
- Selecting a mapplet
- Mapplet transformation field mappings
- Mapplet parameters
- Mapplet transformation output fields
- Mapplet transformation names
- Synchronizing a mapplet
Normalizer transformation
- Normalized fields
- Normalizer field mapping
  - Normalizer field mapping options
- Advanced properties
- Target configuration for Normalizer transformations
- Normalizer field rule for parameterized sources
- Mapping example with a Normalizer and Aggregator
Output transformation
- Output fields
- Field mapping
Parse transformation
- Parse transformation configuration
- Parse transformation field mappings
- Parse transformation output fields
- Advanced properties
Python transformation
- Install and configure Python
- Python transformation fields
- Active and passive Python transformations
- Resource files
- Developing the Python code
  - Creating Python code snippets
  - Referencing a resource file
- Example: Add an ID column to nonpartitioned data
- Example: Use partitions to find the highest salary
- Example: Operationalize a pre-trained model
Rank transformation
- Ranking string values
- Rank caches
- Defining a Rank transformation
- Rank transformation fields
- Defining rank properties
- Defining rank groups
- Advanced properties
- Hierarchical data in advanced mode
- Rank transformation example
Router transformation
- Working with groups
  - Guidelines for connecting output groups
- Group filter conditions
  - Configuring a group filter condition
- Advanced properties
- Hierarchical data in advanced mode
- Router transformation examples
Rule Specification transformation
- Rule Specification transformation configuration
- Rule Specification transformation field mappings
- Rule Specification transformation output fields
- Advanced properties
Sequence Generator transformation
- Sequence Generator transformation uses
- Sequence Generator output fields
- Sequence Generator properties
  - Disabling incoming fields
- Hierarchical data in advanced mode
- Sequence Generator transformation rules and guidelines
- Sequence Generator transformation example
Sorter transformation
- Sort conditions
- Sorter caches
- Advanced properties
- Hierarchical data in advanced mode
- Sorter transformation example
SQL transformation
- Stored procedure or function processing
- Connected or unconnected SQL transformation for stored procedure processing
- Unconnected SQL transformations
- Query processing
- SQL transformation configuration
Structure Parser transformation
- Intelligent structure models
- Structure Parser field mapping
- Output fields
- Advanced properties
- Rules and guidelines for using a Structure Parser transformation
- Rules and guidelines for output types
- Structure Parser transformation configuration
- Processing input from a Hadoop Files source
- Processing input from a flat file source
  - Configuring the flat file source
  - Configuring the Structure Parser transformation to access flat files
- Structure Parser transformation example
Transaction Control transformation
- Transaction control condition
- Using Transaction Control transformations in mappings
  - Sample transaction control mappings with multiple targets
- Guidelines for using Transaction Control transformations in mappings
- Advanced properties
Union transformation
- Comparison to Joiner transformation
- Planning to use a Union transformation
- Input groups
- Output fields
- Field mappings
- Advanced properties
- Union Transformation example
Velocity transformation
- Velocity transformation input format
  - Source configuration for file sources
- Velocity template
- Testing the template
- Velocity transformation output
  - Target configuration for file targets
- Velocity transformation parsers
- Examples
  - XML conversion example
  - JSON conversion example
Verifier transformation
- Address Reference Data
- Verifier transformation configuration
- Verifier transformation field mappings
  - Understanding input and output mappings
- Verifier transformation output fields
- Advanced properties
Web Services transformation
- Create a Web Services consumer connection
- Define a business service
- Configure the Web Services transformation
- Web Services transformation example
- Multibyte hierarchical data

Transformations

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Deduplication and consolidation operations

When you run a mapping with a Deduplicate transformation, the transformation analyzes the identity data in each input record. The transformation generates a set of percentage scores that represent the degrees of similarity between the input records. If two or more records match one another with scores that exceed a given threshold, the transformation considers the records to be duplicates.

The deduplicate asset that you add to the transformation specifies the comparison criteria for the deduplication operation, including the threshold score that duplicate records must meet.

Consolidation is an optional process that the deduplicate asset can specify for the transformation. During consolidation, the transformation evaluates the sets of matching records that the deduplication process identifies. The transformation selects or constructs a preferred version of the records in each set.

Data Quality

user configures the deduplication and consolidation processes in the deduplicate asset. For more information about the criteria that the asset defines, contact the

Data Quality

user.

Rules and guidelines for deduplication and consolidation

When you add a Deduplicate transformation to a mapping, consider the following rules and guidelines:

Mapping fields for identity analysis: The deduplicate asset that you add to the transformation specifies a type of identity, such as a person name or an organization name. The asset identifies the identity type as the objective of the deduplication operations. The type of identity on the asset defines the types of information that the transformation expects to find in the input fields.

You must map the appropriate input fields on the transformation to the target fields that the transformation indicates. You can optionally map the optional input fields to other fields.
Scores and thresholds: The Deduplicate transformation calculates a score for each possible pair of records in the input data. The transformation returns the scores for the records within each set of matching duplicate records. It does not return the scores for records that do not belong in the same set.

The transformation represents the relationships between the records in a matching set as a link score and a driver score.
Sequence ID and group key fields: On the
Field Mapping
tab, the transformation adds a group key field and a sequence ID field to the fields that the asset specifies. The group key field is mandatory. The sequence ID field mandatory in advanced mode..

The group key is a data value that allows the transformation to sort the input records into subsets and to perform discrete duplicate analyses on each subset. When you select a suitable group key, you reduce the time that the mapping takes to run without reducing the quality of the mapping results. If you do not want to divide the input records into groups, add a field to the input data that contains a single or constant value and select the field as the group key.; The sequence ID values determine the order in which the transformation reads the input records. If your input records do not contain a sequence ID field, the transformation reads the records in the order in which they appear in the input data set.
Metadata fields: On the
Output Fields
tab, the transformation adds fields that display the score values for pairs of matching records. The fields also identify the set of matching records to which each record belongs. If the deduplicate asset specifies a consolidation process, the metadata fields specify a preferred record for each record set. The transformation identifies the preferred record as the survivor record.

Use the fields to understand the mapping results.