---
sourceDocument: Australia Workflow Data Fabric
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/integrate-applications

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Workflow Data Fabric

ft:clusterId :

    - crint

bundleId :

    - crint

workflow :

    - Creator


---

# Configure a Transform Map consumer

# Configure a Transform Map consumer {#ariaid-title1}

Release version: Australia  
Updated March 12, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read  
Import and process data from your Kafka environment using your existing transform map
configurations.

## Before you begin

* Role required: integration_hub_admin
* This consumer requires a Stream Connect subscription. For more information, see <https://www.servicenow.com/now-platform/workflow-data-fabric.html>.
* The ServiceNow Stream Connect Installer \[com.glide.hub.stream_connect.installer\] plugin is required.
* This consumer requires a [transform
  map](https://www.servicenow.com/docs/eY~hb42v93guk4uZypzHRA "A transform map is a set of field maps that determine the relationships between fields in an import set and fields in an existing ServiceNow table, such as Incident [incident] or User [sys_user].").
{#configure-transform-map-consumer__ul_mcl_f4x_hvb}

## Formatting Kafka message payloads {#configure-transform-map-consumer__section_dsk_hwt_pfc}

Use simple JSON payloads for Kafka messages. Each Kafka message should have just one, flat JSON object. Complex JSON payloads, such as those with lists and nested objects, are not supported.  
Example of simple, valid JSON input, using a flat map of key-value pairs.

    {"key1": "value1", "key2": "value2"}

Examples of complex, invalid JSON input. The first uses a list. The second uses nested objects.

    [{"key1": "value1", "key2": "value2"}, {"key1": "value3", "key2": "value4"}]

    {"key1": "value1", "key2": {"key3": "value3", "key4": "value4"}}

## About this task

To configure a consumer, you need to create two records.

1. The consumer record, which specifies how to import and process data.
2. A record for the Kafka stream, which defines the stream of data to your consumer.

{#configure-transform-map-consumer__ol_orv_nx1_mvb}This task covers the consumer creation. For instructions on creating a Kafka stream, see [Create a Kafka stream](https://www.servicenow.com/docs/HsNQb6D2P5xrUhQuINaI1Q "Define a data stream for an Extract Transform Load (ETL), Transform Map, or Script consumer. A Kafka stream defines the stream of data to your consumer.").

## Procedure

1. Navigate to AllIntegrationHubConsumersTransform Map Consumer.
2. Select New.
3. In the form, fill in the fields.  
   {#configure-transform-map-consumer__table_v2m_wlq_hvb__entry__2}

   | Field | Description |
   |-|-|
   | Name | Name of the Transform Map consumer. |
   | Transform Map | Name of the transform map to use to process data. |
   | Delivery guarantee | If there's a node failure, option to specify the delivery guarantee for incoming messages. Select one of the following. * No lost but duplicates: All messages are delivered at least once. Some messages might be delivered more than once. * Once or not at all: A message isn't delivered more than once. Some messages might not be delivered at all. {#configure-transform-map-consumer__ul_ojr_cmq_hvb} |
   | Serialization format | The serialization format for the message. Select one of the following. * Plain Text: Select this option for any plain-text messages. This is the default format. * Encoded: Select this option for messages in an Apache Avro format. Converting plain-text messages to an Avro format requires a schema. Select the schema registry in the Schema registry field. For more information on schemas, see [Schema management in Stream Connect](https://www.servicenow.com/docs/vXF5rE~GjaCLi6zZ1vveVA "Import and create schemas to send and receive messages in an Apache Avro format. Using an Avro format can reduce the size of the payload and simplify your integration to your local Kafka instance."). {#configure-transform-map-consumer__ul_svj_dgf_gbc} |
   | Column mapping | Option to specify whether the message's JSON key maps the data to the column's name or label in the import set table. Select one of the following. * Label * Column name {#configure-transform-map-consumer__ul_rch_fmq_hvb} |
   | Synchronize Inserts | Option to guarantee there's only one record with unique coalesce field values by synchronizing record inserts. |
   | Application | Application scope for the Transform Map Consumer. |
   | Schema registry | Registry for the selected schema. Select one of the following. * Standalone Schema Registry * Confluent Schema Registry {#configure-transform-map-consumer__ul_gsw_qff_gbc} This field appears only when the Serialization format is set to Encoded. For the Confluent Schema Registry, if the received message's schema ID isn't in the schema table, the system imports the schema dynamically, using the configured REST connection. |
   [Table 1. Transform Map Consumer]

   {#configure-transform-map-consumer__table_v2m_wlq_hvb}
4. Select Save.

## What to do next

[Create a Kafka stream](https://www.servicenow.com/docs/HsNQb6D2P5xrUhQuINaI1Q "Define a data stream for an Extract Transform Load (ETL), Transform Map, or Script consumer. A Kafka stream defines the stream of data to your consumer.") for this consumer. After the stream is
activated, you can start receiving messages from your Kafka environment.

*[\>]: and then