Create mainframe replication - Precisely Data Integrity Suite

Data Integrity Suite

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Spatial_Analytics

Data_Integration

Data_Enrichment

Data_Governance

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Data_Observability

Data_Quality

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Services

Spatial Analytics

Data Integration

Data Enrichment

Data Governance

Geo Addressing

Data Observability

Data Quality

Core Foundation

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Data Integrity Suite

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en-US

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pt_product_guide

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2000

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2026

Create replication pipelines between mainframe data sources and target systems.

Supported source-to-target combinations:

What's changed: In the new user experience, the mainframe replication pipeline creation options include Include sample files for adding sample configuration files and Auto start for starting the pipeline automatically when the runtime engine starts. For more information on the new experience, see About the new user experience.

VSAM, IMS, or Db2 for z/OS to Kafka

Captures changes from VSAM, IMS, or Db2 for z/OS and streams them to Kafka topics.

Go to Integration > Mainframe Replication.
Click + Create Pipeline to open the Create Mainframe Pipeline window.
Choose VSAM or IMS or Db2 for z/OS to Kafka and specify the pipeline name and description.
Select an existing runtime engine from the dropdown, or click + Add Engine to add a new one.
By default, Include sample files and Automatically start pipeline when runtime engine starts are toggled ON.
Click Create. The new pipeline appears in the table.

Note: For Db2 for z/OS to Kafka pipelines, select the replication type:

Advanced customization: For field transformations, record format changes, filtering, or normalization/denormalization.
High volume replication: For large volumes of data with frequent changes and high transactional throughput.

Kafka, VSAM, or IMS to Db2

Applies changes from Kafka, VSAM, or IMS sources to Db2 target databases. Supported combinations:

Kafka to Db2 for z/OS
VSAM, IMS, or Kafka to Db2 for IBM i

Complete the following pages in the Create Mainframe Pipeline wizard.

General

Provide the following details for your mainframe replication pipeline:

Pipeline Name: Enter a unique name for the pipeline.
Description: Enter a description of the pipeline.

Connections

Select source and target data connections. For each connection you can view:

Catalog Summary: Status of discovery or pending actions.
Cataloged: Number of discovered datasets.
Last Completed: Date and time of the last completed discovery.

Note: If no connections are configured, add them in the Configuration page.

Click Next to proceed.

Source data

The following fields are available on the Source Data page:

Pipeline: Specifies the name of the mainframe pipeline.
Source Connection: Specifies the connection configuration used to access the data source.
Type: Specifies the type of source datasource
Catalog summary: Specifies the status of the data catalog for the source connection. It summarizes whether the cataloging process is complete, in progress, or has encountered issues.
Cataloged: Shows the total count of items that have been cataloged within the data source. It may also indicate any actions needed to complete the cataloging process or address issues with the dataset.
Last Completed: Records the date and time when the most recent data discovery process was completed. This timestamp helps track the freshness of the data and when it was last updated.
Catalog: A clickable option that initiates the cataloging process for datasets within the source connection. It allows users to start or re-start the process of cataloging data for easier management and access.
Search: Provides a search functionality to filter the list of datasets based on names that start with specified characters.
Topic: Lists the columns available in the dataset.
Last updated: Displays the date and time when the discovery table for the source data was last updated.

Replication fields

The data sources you select capture changes to your source data, such as inserts, updates, and deletes. For effective data replication, select the appropriate producer for the source messages and map each field in the source data to the corresponding replication fields.

Different mechanisms for capturing and replicating changes include Precisely Replication, Debezium, or custom solutions, depending on the data source and replication requirements.

Precisely Replication: A system that uses proprietary connectors to capture and replicate changes in real-time or batch processes.
Debezium: An open-source platform that streams changes from database logs to Kafka topics. For more information, see the Debezium documentation.
Custom change capture: Refers to tailored solutions that track changes using methods such as database triggers or custom logs, addressing specific needs or constraints.

Verify field assignment to maintain data consistency and integrity throughout the replication process. Accurate mapping of source fields to replication fields confirms that changes are applied correctly to the target system.

Table 1. Source message producer fields
Precisely Replication	Debezium	Custom
Row operation: Type of operation performed on the row.	Row operation: Type of operation performed on the row.	Row operation: Type of operation performed on the row.
Row timestamp: GMT timestamp of the update in `YYYYMMDDHHMMSSffffff` format.	Row timestamp: GMT timestamp of the update in `YYYYMMDDHHMMSSffffff` format.	Row timestamp: GMT timestamp of the update in `YYYYMMDDHHMMSSffffff` format.
Dataset name: Name of the dataset for data synchronization.	Table name: Name of the table.	Dataset name: Name of the dataset for data synchronization.
DBMS type: Category of the DBMS used in the pipeline.	Schema name: Name of the schema.	Transaction ID: 16-digit unique identifier of the transaction.
Server name: Name of the server hosting the mainframe system.	Database name: Name of the database.	Transaction row sequence: Position of the row in the chronological sequence of operations within the transaction.
Transaction ID: 16-digit unique identifier of the transaction.	DBMS type: Category of the DBMS used in the pipeline.	Transaction timestamp: Transaction commit timestamp (log-based) or last update time for replication (trigger-based), in GMT.
Transaction row sequence: Position of the row in the chronological sequence of operations within the transaction.	Transaction ID: 16-digit unique identifier of the transaction.	Before image: State of the data before modifications.
Transaction timestamp: Transaction commit timestamp (log-based) or last update time for replication (trigger-based), in GMT.	Transaction row sequence: Position of the row in the chronological sequence of operations within the transaction.	After image: State of the data after the transaction completes.
Transaction username: Username associated with the transaction.	Transaction timestamp: Transaction commit timestamp (log-based) or last update time for replication (trigger-based), in GMT.	DBMS type: Category of the DBMS used in the pipeline.
Before image: State of the data before modifications.	Transaction username: Username associated with the transaction.	Server name: Name of the server hosting the mainframe system.
After image: State of the data after the transaction completes.	Before image: State of the data before modifications.	Transaction username (optional): Username associated with the transaction.
Table name (optional): Name of the table.	After image: State of the data after the transaction completes.	Table name (optional): Name of the table.
Schema name (optional): Name of the schema.	Dataset name (optional): Name of the dataset.	Schema name (optional): Name of the schema.
Database name (optional): Name of the database.	Server name (optional): Name of the server.	Database name (optional): Name of the database.

Note: For Custom change capture and Debezium, you must specify how to map the metadata fields in your JSON messages to Data Integrity Suite metadata fields.

Target data

Search: Filters schemas and datasets by name prefix.
Schema: Available schema structures in the data connection.
Datasets Selected: Number of datasets selected per schema.
Dataset: Datasets available through the target connection.
Last updated: When the target discovery table was last updated.

Mapping

Map source dataset names to target datasets. Source names default to target names until replication starts; verify accuracy before proceeding.

Change Mapping: Opens a dialog to define schema and dataset mappings.
- x out of y rows selected: Count of rows selected for mapping.
- Specify a pattern: Combines source field tokens with text to define the data structure.
- Specify value: Appears when the dropdown is set to Specify; define specific values for the mapping.
- Apply: Saves and applies mapping changes for selected rows.
- Close: Closes the dialog without saving.
Reset to Default: Reverts selected mappings to defaults.
Source: Source schemas and datasets.
Target: Target schemas and datasets.

Summary

Choose the actions to perform, then click Finish:

Stage Configuration Changes: Saves changes for review without applying them immediately.
Make Configurations Changes Active: Deploys and activates staged changes.
Start Replication Pipeline: Starts the pipeline with the new configuration.

The wizard provides progress feedback. On success, the updated pipeline appears in the list. If errors occur, the wizard remains open with an error message.