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
- Joiner transformation example
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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Aggregator transformation example

You need to retrieve the latest price for every stock in your change data capture (CDC) system.

To retrieve the most recent price for each stock, create a mapping and add an Aggregator transformation and Joiner transformation.

Configure the Aggregator transformation to group all the stocks by stock ID and calculate the latest trading time for each stock ID.

Configure the Joiner transformation to perform a self join with the stock ID and trading time, using the source data and Aggregator transformation output.

The Target transformation writes the stock ID and price to the target file.

Source data

The following table shows the source data:

STOCK_ID	PRICE	VOLUME	TRADETIME
100	1.12	125	2023.01.20.1245
105	3.45	56	2023.01.18.0821
102	2.29	25	2023.01.19.1118
101	4.56	87	2023.01.17.0901
102	2.35	38	2023.01.20.1649
100	0.99	94	2023.01.20.1355
104	1.88	67	2023.01.20.1730
103	2.11	41	2023.01.17.1211
105	3.40	90	2023.01.18.1525
104	3.40	90	2023.01.20.1634
103	3.40	90	2023.01.20.1526
105	3.40	90	2023.01.19.1745

Mapping configuration

The following image shows the mapping that you configure:

A mapping that uses the Aggregator transformation. The mapping includes the following transformations, in order: Source, Aggregator, Joiner, Target. The Source transformation connects to both the Aggregator and Joiner transformations. The Aggregator transformation connects to the Joiner transformation. The Joiner transformation connects to the Target transformation.

Configure the transformations in the following ways:

Aggregator transformation: The following table describes the properties to configure in the Aggregator transformation:

Properties
Configuration

Incoming Fields
Configure a field rule to prefix all incoming fields with "
agg_
" so that the aggregated fields have unique names.

Group By
Group by the field
agg_stock_id
.

Aggregate
Create one aggregate field with the following properties to return the latest trading time for each stock ID:
Field Type: Output Field
Name: agg_maxtime
Type: string
Precision: 10

Configure the following expression:
max(agg_tradetime)

Properties	Configuration
Incoming Fields	Configure a field rule to prefix all incoming fields with " agg_ " so that the aggregated fields have unique names.
Group By	Group by the field agg_stock_id .
Aggregate	Create one aggregate field with the following properties to return the latest trading time for each stock ID: Field Type: Output Field Name: agg_maxtime Type: string Precision: 10 Configure the following expression: max(agg_tradetime)

Joiner transformation: In the Joiner transformation, configure a normal join with the following join conditions:
agg_stock_id = STOCK_ID

agg_maxtime = TRADETIME

Target transformation: The following table describes the properties to configure in the Target transformation:

Properties
Configuration

Incoming Fields
Configure the following field rules:
Include all fields.
Exclude
Fields by Text or Pattern with the prefix "agg_"
.

The list of included fields shows all the source fields: PRICE, STOCK_ID, TRADETIME, VOLUME

Target Fields
Add the following target fields: STOCK_ID and PRICE

Field Mapping
Perform an automap with
Exact Field Name
. This maps the incoming fields STOCK_ID and PRICE to the target fields STOCK_ID and PRICE.

Properties	Configuration
Incoming Fields	Configure the following field rules: Include all fields. Exclude Fields by Text or Pattern with the prefix "agg_" . The list of included fields shows all the source fields: PRICE, STOCK_ID, TRADETIME, VOLUME
Target Fields	Add the following target fields: STOCK_ID and PRICE
Field Mapping	Perform an automap with Exact Field Name . This maps the incoming fields STOCK_ID and PRICE to the target fields STOCK_ID and PRICE.

Target Data

The following table shows the data that the mapping writes to the target file:

STOCK_ID	PRICE
100	0.99
101	4.56
102	2.35
103	3.40
104	3.40
105	3.40

Since the fields are grouped by STOCK_ID in the Aggregator transformation, each stock ID is listed in one row with its most recent price. For example, the most recently traded price for stock 105 is $3.40.