DocumentationExperience PlatformSource Connectors Guide

[Beta]{class="badge informative"}

Create a source connection and dataflow to stream LAVA data using the Flow Service API

Last update: May 14, 2026

CREATED FOR:

  • Developer
AVAILABILITY
The LAVA source is in beta. Read the terms and conditions in the sources overview for more information on using beta-labeled sources.

Getting started

This guide requires a working understanding of the following components of Experience Platform:

  • Sources: Use Sources in Experience Platform to easily bring in data from a variety of systems. Sources help you gather, organize, and prepare your data so you can make the most of Experience Platform’s capabilities.
  • Sandboxes: Sandboxes let you safely build, test, and experiment in Experience Platform without affecting your production data. They create separate environments so you can try things out, develop new features, or collaborate with your team risk-free.
TIP
Before starting this tutorial, review the LAVA source connector overview to make sure you meet all prerequisites.

Load the LAVA package

LAVA provides a package that includes our recommended field groups, schemas, identity namespace and datasets for using LAVA in Experience Platform. Use of these packages is recommended, but not required.

Read this section to learn how to import this into your sandbox and get the IDs required for later steps.

API format

POST /transfer/pullRequest

Request

The following request loads the package for LAVA:

curl -X POST \
  'https://platform.adobe.io/data/foundation/exim/transfer/pullRequest' \
  -H 'Authorization: Bearer {ACCESS_TOKEN}' \
  -H 'x-api-key: {API_KEY}' \
  -H 'x-gw-ims-org-id: {ORG_ID}' \
  -H 'x-sandbox-name: {SANDBOX_NAME}' \
  -H 'Content-Type: application/json' \
  -d '{
    "imsOrgId": "1EF71E43679AAD1E0A495C77@AdobeOrg",
    "packageId": "416a0c2a32794092aa1a957cbe9a6698"
  }'
Property
Description
Type
Required
imsOrgId
The ID from the package’s source organization. Must be 1EF71E43679AAD1E0A495C77@AdobeOrg.
String
Yes
packageId
The ID from the package to import. Must be 416a0c2a32794092aa1a957cbe9a6698.
String
Yes

Response

A successful response returns details on the imported public package.

{
    "id": "{ID}",
    "version": 0,
    "createdDate": 1729658890425,
    "modifiedDate": 1729658890425,
    "createdBy": "{CREATED_BY}",
    "modifiedBy": "{MODIFIED_BY}",
    "sourceIMSOrgId": "{ORG_ID}",
    "targetIMSOrgId": "{TARGET_ID}",
    "packageId": "{PACKAGE_ID}",
    "status": "PENDING",
    "initiatedBy": "{INITIATED_BY}",
    "pipelineMessageId": "{MESSAGE_ID}",
    "requestType": "PUBLIC"
}

Retrieve your schemas

After importing the package, retrieve the LAVA Events and LAVA Profile schemas:

Request

curl -X GET \
  'https://platform.adobe.io/data/foundation/schemaregistry/tenant/schemas?name=LAVA*' \
  -H 'Authorization: Bearer {{ACCESS_TOKEN}}' \
  -H 'x-api-key: {{API_KEY}}' \
  -H 'x-gw-ims-org-id: {{ORG_ID}}' \
  -H 'x-sandbox-name: {{SANDBOX_NAME}}' \
  -H 'Accept: application/vnd.adobe.xed-id+json'

Response

A successful response returns a list of schemas. Use these IDs as target XDM schemas in a later step.

{
  "results": [
    ...
    {
      "$id": "https://ns.adobe.com/{TENANT_ID}/schemas/7ff102f217d394e8beff48dcc2c27baae14e28e210d36492",
      "meta:altId": "_{TENANT_ID}.schemas.7ff102f217d394e8beff48dcc2c27baae14e28e210d36492",
      "version": "1.4",
      "title": "LAVA Events"
    },
    {
      "$id": "https://ns.adobe.com/{TENANT_ID}/schemas/991bed7f1d94ccf47bd392bc345ee51e7e0bd19b1de3dbff",
      "meta:altId": "_{TENANT_ID}.schemas.991bed7f1d94ccf47bd392bc345ee51e7e0bd19b1de3dbff",
      "version": "1.2",
      "title": "LAVA Profile"
    },
    //...
  ]
}

Retrieve your datasets

Next, use the following calls to retrieve your dataset IDs.

Request

curl -X GET \
  'https://platform.adobe.io/data/foundation/catalog/dataSets?name=LAVA*' \
  -H 'Authorization: Bearer {{ACCESS_TOKEN}}' \
  -H 'x-api-key: {{API_KEY}}' \
  -H 'x-gw-ims-org-id: {{ORG_ID}}' \
  -H 'x-sandbox-name: {{SANDBOX_NAME}}' \
  -H 'Accept: application/json'

Response

{
  "6920eb494131bfcc10305302": {
    "id": "6920eb494131bfcc10305302",
    "name": "LAVA Member Profiles",
    //...
  },
  "6920eb7565bd3ed93a35cd0e": {
    "id": "6920eb7565bd3ed93a35cd0e",
    "name": "LAVA Member Rewards",
    //...
  },
  "6924aecd8d9c85e2d56261e3": {
    "id": "6924aecd8d9c85e2d56261e3",
    "name": "LAVA Events",
    "schemaRef": {
      "id": "https://ns.adobe.com/{TENANT_ID}/schemas/7ff102f217d394e8beff48dcc2c27baae14e28e210d36492",
      "contentType": "application/vnd.adobe.xed-full+json;version=1"
    },
    //...
  }
}

Connect LAVA to Experience Platform using the Flow Service API

The following tutorial walks you through the steps to create a LAVA source connection and create a dataflow to bring LAVA data to Experience Platform using the Flow Service API.

Create a source connection source-connection

Create a source connection by making a POST request to the Flow Service API, while providing the connection spec ID of your source, details like name and description, and the format of your data.

API format

POST /sourceConnections

Request

The following request creates a source connection for LAVA:

curl -X POST \
  'https://platform.adobe.io/data/foundation/flowservice/sourceConnections' \
  -H 'Authorization: Bearer {ACCESS_TOKEN}' \
  -H 'x-api-key: {API_KEY}' \
  -H 'x-gw-ims-org-id: {ORG_ID}' \
  -H 'x-sandbox-name: {SANDBOX_NAME}' \
  -H 'Content-Type: application/json' \
  -d '{
      "name": "Streaming Source Connection for a Streaming SDK source",
      "description": "Streaming Source Connection for a Streaming SDK source",
      "connectionSpec": {
          "id": "232dfabe-27aa-41c0-a7cf-9961661dc68b",
          "version": "1.0"
      },
      "data": {
          "format": "json"
      }
    }'
Property
Description
name
The name of your source connection. Ensure that the name of your source connection is descriptive as you can use this to look up information on your source connection.
description
An optional value that you can include to provide more information on your source connection.
connectionSpec.id
The connection specification ID that corresponds to your source. Must be 232dfabe-27aa-41c0-a7cf-9961661dc68b for LAVA.
data.format
The format of the LAVA data that you want to ingest. Currently, the only supported data format is json.

Response

A successful response returns the unique identifier (id) of the newly created source connection. This ID is required in a later step to create a dataflow.

{
     "id": "246d052c-da4a-494a-937f-a0d17b1c6cf5",
     "etag": "\"712a8c08-fda7-41c2-984b-187f823293d8\""
}

Create a target XDM schema target-schema

IMPORTANT
Skip this if you imported the LAVA package, as this includes target XDM schemas.

In order for the source data to be used in Experience Platform, a target schema must be created to structure the source data according to your needs. The target schema is then used to create an Experience Platform dataset in which the source data is contained. If you are using multiple LAVA sets of data, for example both member balances and ticket scan events, you may want or need more than one target XDM schema.

A target XDM schema can be created by performing a POST request to the Schema Registry API.

For detailed steps on how to create a target XDM schema, see the tutorial on creating a schema using the API.

Create a target dataset target-dataset

IMPORTANT
If you have already imported the LAVA package, you can skip this step because the package provides a target XDM dataset for you. However, if you need multiple LAVA datasets (such as one for member balances and another for ticket scan events), you should create additional target datasets as required.

A target dataset can be created by performing a POST request to the Catalog Service API, providing the ID of the target schema within the payload.

For detailed steps on how to create a target dataset, see the tutorial on creating a dataset using the API.

Create a target connection target-connection

A target connection represents the connection to the destination where the ingested data is to be stored. To create a target connection, you must provide the fixed connection specification ID that corresponds to the data lake. This ID is: c604ff05-7f1a-43c0-8e18-33bf874cb11c.

You now have the unique identifiers for a target schema, a target dataset, and the connection spec ID for the data lake. Using these identifiers, you can create a target connection using the Flow Service API to specify the dataset that will contain the inbound source data.

API format

POST /targetConnections

Request

The following request creates a target connection for LAVA:

curl -X POST \
  'https://platform.adobe.io/data/foundation/flowservice/targetConnections' \
  -H 'Authorization: Bearer {ACCESS_TOKEN}' \
  -H 'x-api-key: {API_KEY}' \
  -H 'x-gw-ims-org-id: {ORG_ID}' \
  -H 'x-sandbox-name: {SANDBOX_NAME}' \
  -H 'Content-Type: application/json' \
  -d '{
      "name": "Streaming Target Connection for a Streaming SDK source",
      "description": "Streaming Target Connection for a Streaming SDK source",
      "connectionSpec": {
          "id": "c604ff05-7f1a-43c0-8e18-33bf874cb11c",
          "version": "1.0"
      },
      "data": {
          "format": "json",
          "schema": {
              "id": "{TARGET_XDM_SCHEMA}",
              "version": "application/vnd.adobe.xed-full+json;version=1"
          }
      },
      "params": {
          "dataSetId": "{TARGET_DATASET}"
      }
  }'
Property
Description
name
The name of your target connection. Ensure that the name of your target connection is descriptive as you can use this to look up information on your target connection.
description
An optional value that you can include to provide more information on your target connection.
connectionSpec.id
The connection specification ID that corresponds to the data lake. This fixed ID is: c604ff05-7f1a-43c0-8e18-33bf874cb11c.
data.format
The format of the LAVA data that you want to bring to Experience Platform.
params.dataSetId
The target dataset ID retrieved in a previous step.

Response

A successful response returns the new target connection’s unique identifier (id). This ID is required in later steps.

{
     "id": "7c96c827-3ffd-460c-a573-e9558f72f263",
     "etag": "\"a196f685-f5e8-4c4c-bfbd-136141bb0c6d\""
}

Create a mapping mapping

In order for the source data to be ingested into a target dataset, it must first be mapped to the target schema that the target dataset adheres to. This is achieved by performing a POST request to Data Prep API with data mappings defined within the request payload.

When utilizing the schema provided by LAVA, the following mapping is recommended:

Member profiles
code language-json 
{
  "version": 0,
  "xdmSchema": "{TARGET_XDM_SCHEMA}",
  "xdmVersion": "1.0",
  "mappings": [
    {
      "destinationXdmPath": "_{TENANT_ID}.lavaId",
      "sourceAttribute": "lavaId",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "person.name.firstName",
      "sourceAttribute": "firstName",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "person.name.lastName",
      "sourceAttribute": "lastName",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "personalEmail.address",
      "sourceAttribute": "email",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "mobilePhone.number",
      "sourceAttribute": "phone",
      "identity": false,
      "version": 0
    }
  ]
}
Member balances
code language-json 
{
  "version": 0,
  "xdmSchema": "{TARGET_XDM_SCHEMA}",
  "xdmVersion": "1.0",
  "mappings": [
    {
      "destinationXdmPath": "_{TENANT_ID}.lavaId",
      "sourceAttribute": "lavaId",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.balances",
      "sourceAttribute": "balances",
      "identity": false,
      "version": 0
    }
  ]
}
Ticket scan events
code language-json 
{
  "version": 0,
  "xdmSchema": "{TARGET_XDM_SCHEMA}",
  "xdmVersion": "1.0",
  "mappings": [
    {
      "destinationXdmPath": "identityMap",
      "sourceExpression": "to_map(\"LavaId\",to_array(false,to_object(\"id\",lavaId,\"primary\",true)))",
      "sourceType": "EXPRESSION",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.eventId",
      "sourceAttribute": "eventId",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.eventName",
      "sourceAttribute": "eventName",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.eventLabel",
      "sourceAttribute": "eventLabel",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.venue",
      "sourceAttribute": "venue",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.venueLabel",
      "sourceAttribute": "venueLabel",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.section",
      "sourceAttribute": "section",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.sectionLabel",
      "sourceAttribute": "sectionLabel",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.row",
      "sourceAttribute": "row",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.seat",
      "sourceAttribute": "seat",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.gate",
      "sourceAttribute": "gate",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "_{TENANT_ID}.ticketScan.gateLabel",
      "sourceAttribute": "gateLabel",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "eventType",
      "sourceAttribute": "type",
      "identity": false,
      "version": 0
    },
    {
      "destinationXdmPath": "timestamp",
      "sourceAttribute": "timestamp",
      "identity": false,
      "version": 0
    }
  ]
}

API format

POST /conversion/mappingSets

Request

curl -X POST \
  'https://platform.adobe.io/data/foundation/mappingSets' \
  -H 'Authorization: Bearer {ACCESS_TOKEN}' \
  -H 'x-api-key: {API_KEY}' \
  -H 'x-gw-ims-org-id: {ORG_ID}' \
  -H 'x-sandbox-name: {SANDBOX_NAME}' \
  -H 'Content-Type: application/json' \
  -d '{
      "version": 0,
      "xdmSchema": "{TARGET_XDM_SCHEMA}",
      "xdmVersion": "1.0",
      "mappings": [
          {
              "destinationXdmPath": "_{TENANT_ID}.lavaId",
              "sourceAttribute": "lavaId",
              "identity": false,
              "version": 0
          },
          {
              "destinationXdmPath": "_{TENANT_ID}.balances",
              "sourceAttribute": "balances",
              "identity": false,
              "version": 0
          }
      ]
  }'
Property
Description
xdmSchema
The ID of the target XDM schema generated in an earlier step.
mappings.destinationXdmPath
The destination XDM path where the source attribute is being mapped to.
mappings.sourceAttribute
The source attribute that needs to be mapped to a destination XDM path.
mappings.identity
A boolean value that designates whether the mapping set will be marked for Identity Service.

Response

A successful response returns details of the newly created mapping including its unique identifier (id). This value is required in a later step to create a dataflow.

{
    "id": "bf5286a9c1ad4266baca76ba3adc9366",
    "version": 0,
    "createdDate": 1597784069368,
    "modifiedDate": 1597784069368,
    "createdBy": "{CREATED_BY}",
    "modifiedBy": "{MODIFIED_BY}"
}

Create a flow flow

The last step towards bringing data from LAVA to Experience Platform is to create a dataflow. By now, you have the following required values prepared:

A dataflow is responsible for scheduling and collecting data from a source. You can create a dataflow by performing a POST request while providing the previously mentioned values within the payload.

API format

POST /flows

Request

curl -X POST \
  'https://platform.adobe.io/data/foundation/flowservice/flows' \
  -H 'x-api-key: {API_KEY}' \
  -H 'x-gw-ims-org-id: {ORG_ID}' \
  -H 'x-sandbox-name: {SANDBOX_NAME}' \
  -H 'Content-Type: application/json' \
  -d '{
      "name": "Streaming Dataflow for a Streaming SDK source",
      "description": "Streaming Dataflow for a Streaming SDK source",
      "flowSpec": {
          "id": "e77fde5a-22a8-11ed-861d-0242ac120002",
          "version": "1.0"
      },
      "sourceConnectionIds": [
          "246d052c-da4a-494a-937f-a0d17b1c6cf5"
      ],
      "targetConnectionIds": [
          "7c96c827-3ffd-460c-a573-e9558f72f263"
      ],
      "transformations": [
      {
        "name": "Mapping",
        "params": {
          "mappingId": "bf5286a9c1ad4266baca76ba3adc9366",
          "mappingVersion": 0
        }
      }
    ]
  }'
Property
Description
name
The name of your dataflow. Ensure that the name of your dataflow is descriptive as you can use this to look up information on your dataflow.
description
An optional value that you can include to provide more information on your dataflow.
flowSpec.id
The flow specification ID required to create a dataflow. This fixed ID is: e77fde5a-22a8-11ed-861d-0242ac120002.
flowSpec.version
The corresponding version of the flow specification ID. This value defaults to 1.0.
sourceConnectionIds
The source connection ID generated in an earlier step.
targetConnectionIds
The target connection ID generated in an earlier step.
transformations
This property contains the various transformations that need to be applied to your data. This property is required when bringing non-XDM-compliant data to Experience Platform.
transformations.name
The name assigned to the transformation.
transformations.params.mappingId
The mapping ID generated in an earlier step.
transformations.params.mappingVersion
The corresponding version of the mapping ID. This value defaults to 0.

Response

A successful response returns the ID (id) of the newly created dataflow. You can use this ID to monitor, update, or delete your dataflow.

{
     "id": "993f908f-3342-4d9c-9f3c-5aa9a189ca1a",
     "etag": "\"510bb1d4-8453-4034-b991-ab942e11dd8a\""
}

Get your streaming endpoint URL

With your dataflow created, you can now retrieve your streaming endpoint URL. You will use this endpoint URL to subscribe your source to a webhook, allowing your source to communicate with Experience Platform.

To retrieve your streaming endpoint URL, make a GET request to the /flows endpoint and provide the ID of your dataflow.

API format

GET /flows/{FLOW_ID}

Request

curl -X GET \
  'https://platform.adobe.io/data/foundation/flowservice/flows/993f908f-3342-4d9c-9f3c-5aa9a189ca1a' \
  -H 'Authorization: Bearer {ACCESS_TOKEN}' \
  -H 'x-api-key: {API_KEY}' \
  -H 'x-gw-ims-org-id: {ORG_ID}' \
  -H 'x-sandbox-name: {SANDBOX_NAME}'

Response

A successful response returns information on your dataflow, including your endpoint URL, marked as inletUrl.

{
  "items": [
    {
      "id": "993f908f-3342-4d9c-9f3c-5aa9a189ca1a",
      "createdAt": 1669238699119,
      "updatedAt": 1669238699119,
      "createdBy": "acme@AdobeID",
      "updatedBy": "acme@AdobeID",
      "createdClient": "{CREATED_CLIENT}",
      "updatedClient": "{UPDATED_CLIENT}",
      "sandboxId": "{SANDBOX_ID}",
      "sandboxName": "{SANDBOX_NAME}",
      "imsOrgId": "{ORG_ID}",
      "name": "Streaming Dataflow for a Streaming SDK source",
      "description": "Streaming Dataflow for a Streaming SDK source",
      "flowSpec": {
        "id": "e77fde5a-22a8-11ed-861d-0242ac120002",
        "version": "1.0"
      },
      "state": "enabled",
      "version": "\"a1011225-0000-0200-0000-63c78ae60000\"",
      "etag": "\"a1011225-0000-0200-0000-63c78ae60000\"",
      "sourceConnectionIds": [
        "246d052c-da4a-494a-937f-a0d17b1c6cf5"
      ],
      "targetConnectionIds": [
        "7c96c827-3ffd-460c-a573-e9558f72f263"
      ],
      "inheritedAttributes": {
        "properties": {
          "isSourceFlow": true
        },
        "sourceConnections": [
          {
            "id": "246d052c-da4a-494a-937f-a0d17b1c6cf5",
            "connectionSpec": {
              "id": "bdb5b792-451b-42de-acf8-15f3195821de",
              "version": "1.0"
            }
          }
        ],
        "targetConnections": [
          {
            "id": "7c96c827-3ffd-460c-a573-e9558f72f263",
            "connectionSpec": {
              "id": "c604ff05-7f1a-43c0-8e18-33bf874cb11c",
              "version": "1.0"
            }
          }
        ]
      },
      "options": {
        "errorDiagnosticsEnabled": true,
        "inletUrl": "https://dcs-int.adobedc.net/collection/ab65636c31778fb0455c439ffb48a5433a34d443f4c83c4b5beda9c5688797c5"
      },
      "transformations": [
        {
          "name": "Mapping",
          "params": {
            "mappingVersion": 0,
            "mappingId": "bf5286a9c1ad4266baca76ba3adc9366"
          }
        }
      ],
      "runs": "/runs?property=flowId==e1514b79-f031-43b4-aab5-381a42f86ad4",
      "providerRefId": "c9809ab5-71e0-4c7f-887b-61c95e4e20b5",
      "lastOperation": {
        "started": 0,
        "updated": 0,
        "operation": "enable"
      }
    }
  ]
}

Integrate LAVA with your webhook

In the LAVA Console, navigate to Resources > Data Export.

Screenshot of the "Data Export" menu in LAVA Console

Select Create New Export. Select Adobe Source Connector as the destination type, and the desired source data to send. Use the streaming endpoint URL and dataflow ID.

Screenshot of the "Create New Export" screen in LAVA Console

Appendix

The following section provides information on the steps you can take to monitor, update, and delete your dataflow.

Monitor your dataflow

Once your dataflow has been created, you can monitor the data that is being ingested through it to see information on flow runs, completion status, and errors. For complete API examples, read the guide on monitoring your sources dataflows using the API.

Update your dataflow

Update the details of your dataflow, such as its name and description, as well as its run schedule and associated mapping sets by making a PATCH request to the /flows endpoint of the Flow Service API, while providing the ID of your dataflow. When making a PATCH request, you must provide your dataflow’s unique etag in the If-Match header. For complete API examples, read the guide on updating sources dataflows using the API.

Update your account

Update the name, description, and credentials of your source account by performing a PATCH request to the Flow Service API while providing your base connection ID as a query parameter. When making a PATCH request, you must provide your source account’s unique etag in the If-Match header. For complete API examples, read the guide on updating your source account using the API.

Delete your dataflow

Delete your dataflow by performing a DELETE request to the Flow Service API while providing the ID of the dataflow you want to delete as part of the query parameter. For complete API examples, read the guide on deleting your dataflows using the API.

Delete your account

Delete your account by performing a DELETE request to the Flow Service API while providing the base connection ID of the account you want to delete. For complete API examples, read the guide on deleting your source account using the API.

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