Create a dataflow for E-commerce sources using the Flow Service API
This tutorial covers the steps for retrieving data from an e-commerce source and bringing them to Platform using .
- In order to create a dataflow, you must already have a valid base connection ID with an E-commerce source. If you do not have this ID, then see the sources overview for a list of E-commerce sources that you can create a base connection with.
- For Experience Platform to ingest data, timezones for all table-based batch sources must be configured to UTC.
Getting started
This tutorial requires you to have a working understanding of the following components of ۶Ƶ Experience Platform:
-
Experience Data Model (XDM) System: The standardized framework by which Experience Platform organizes customer experience data.
- Basics of schema composition: Learn about the basic building blocks of XDM schemas, including key principles and best practices in schema composition.
- Schema Registry API: Learn how to successfully perform calls to the Schema Registry API. This includes your
{TENANT_ID}, the concept of “containers”, and the required headers for making requests (with special attention to the Accept header and its possible values).
-
Catalog Service: Catalog is the system of record for data location and lineage within Experience Platform.
-
Batch ingestion: The Batch Ingestion API allows you to ingest data into Experience Platform as batch files.
-
Sandboxes: Experience Platform provides virtual sandboxes which partition a single Platform instance into separate virtual environments to help develop and evolve digital experience applications.
Using Platform APIs
For information on how to successfully make calls to Platform APIs, see the guide on getting started with Platform APIs.
Create a source connection source
You can create a source connection by making a POST request to the Flow Service API. A source connection consists of a connection ID, a path to the source data file, and a connection spec ID.
To create a source connection, you must also define an enum value for the data format attribute.
Use the following enum values for file-based connectors:
delimitedjsonparquetFor all table-based connectors, set the value to tabular.
API format
POST /sourceConnections
Request
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": "Shopify source connection",
"baseConnectionId": "582f4f8d-71e9-4a5c-a164-9d2056318d6c",
"description": "Shopify source connection",
"data": {
"format": "tabular"
},
"params": {
"tableName": "Shopify.Orders",
"columns": [
{
"name": "Email",
"type": "string"
},
{
"name": "Phone",
"type": "string"
},
]
},
"connectionSpec": {
"id": "4f63aa36-bd48-4e33-bb83-49fbcd11c708",
"version": "1.0"
}
}'
baseConnectionIdparams.pathconnectionSpec.idResponse
A successful response returns the unique identifier (id) of the newly created source connection. This ID is required in later steps to create a target connection.
{
"id": "c278ab14-acdf-440b-b67f-1265d15a7655",
"etag": "\"10007c3f-0000-0200-0000-5fa9be720000\""
}
Create a target XDM schema target-schema
In order for the source data to be used in Platform, a target schema must be created to structure the source data according to your needs. The target schema is then used to create a Platform dataset in which the source data is contained.
A target XDM schema can be created by performing a POST request to the .
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
A target dataset can be created by performing a POST request to the , 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 lands in. To create a target connection, you must provide the fixed connection spec ID associated to the Data Lake. This connection spec ID is: c604ff05-7f1a-43c0-8e18-33bf874cb11c.
You now have the unique identifiers a target schema a target dataset and the connection spec ID to data lake. Using the Flow Service API, you can create a target connection by specifying these identifiers along with the dataset that will contain the inbound source data.
API format
POST /targetConnections
Request
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": "Shopify target connection",
"description": "Shopify target connection",
"data": {
"format": "parquet_xdm",
"schema": {
"id": "https://ns.adobe.com/{TENANT_ID}/schemas/854ddc36ad2c7bd001f66a4392575ed4004f81883328772f",
"version": "application/vnd.adobe.xed-full-notext+json; version=1"
}
},
"params": {
"dataSetId": "5fa9c083de62e418dd170b42"
},
"connectionSpec": {
"id": "c604ff05-7f1a-43c0-8e18-33bf874cb11c",
"version": "1.0"
}
}'
data.schema.id$id of the target XDM schema.data.schema.versionapplication/vnd.adobe.xed-full+json;version=1, which returns the latest minor version of the schema.params.dataSetIdconnectionSpec.idc604ff05-7f1a-43c0-8e18-33bf874cb11c.Response
A successful response returns the new target connection’s unique identifier (id). This value is required in a later step to create a dataflow.
{
"id": "6c0ba537-a96b-4d74-8c95-450eb88baee8",
"etag": "\"00005506-0000-0200-0000-5fa9c13c0000\""
}
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.
To create a mapping set, make a POST request to the mappingSets endpoint of the while providing your target XDM schema $id and the details of the mapping sets you want to create.
API format
POST /mappingSets
Request
curl -X POST \
'https://platform.adobe.io/data/foundation/conversion/mappingSets' \
-H 'Authorization: Bearer {ACCESS_TOKEN}' \
-H 'x-api-key: {API_KEY}' \
-H 'x-gw-ims-org-id: {ORG_ID}' \
-H 'x-sandbox-name: {SANDBOX_NAME}' \
-H 'Content-Type: application/json' \
-d '{
"version": 0,
"xdmSchema": "https://ns.adobe.com/{TENANT_ID}/schemas/854ddc36ad2c7bd001f66a4392575ed4004f81883328772f",
"xdmVersion": "1.0",
"id": null,
"mappings": [
{
"destinationXdmPath": "personalEmail.address",
"sourceAttribute": "Email",
"identity": false,
"version": 0
},
{
"destinationXdmPath": "mobilePhone.number",
"sourceAttribute": "Shipping_Address_Phone",
"identity": false,
"version": 0
}
]
}'
xdmSchema$id of the target XDM schema.Response
A successful response returns details of the newly created mapping including its unique identifier (id). This ID is required in a later step to create a dataflow.
{
"id": "22922102bffd4369b6209c102a604062",
"version": 0,
"createdDate": 1604960750613,
"modifiedDate": 1604960750613,
"createdBy": "{CREATED_BY}",
"modifiedBy": "{MODIFIED_BY}"
}
Look up dataflow specifications specs
A dataflow is responsible for collecting data from sources and bringing them into Platform. In order to create a dataflow, you must first obtain the dataflow specifications by performing a GET request to the Flow Service API. Dataflow specifications are responsible for collecting data from an e-commerce source.
API format
GET /flowSpecs?property=name=="CRMToAEP"
Request
curl -X GET \
'https://platform.adobe.io/data/foundation/flowservice/flowSpecs?property=name=="CRMToAEP"' \
-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 the details of the dataflow specification responsible for bringing data from your source into Platform. The response includes the unique flow spec id required to create a new dataflow.
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Create a dataflow
The last step towards collecting data is to create a dataflow. At this point, you should 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 request payload.
To schedule an ingestion, you must first set the start time value to epoch time in seconds. Then, you must set the frequency value to one of the five options: once, minute, hour, day, or week. The interval value designates the period between two consecutive ingestions and creating a one-time ingestion does not require an interval to be set. For all other frequencies, the interval value must be set to equal or greater than 15.
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": "Test Shopify dataflow",
"description": "Shopify With mapping ingestion",
"flowSpec": {
"id": "14518937-270c-4525-bdec-c2ba7cce3860",
"version": "1.0"
},
"sourceConnectionIds": [
"c278ab14-acdf-440b-b67f-1265d15a7655"
],
"targetConnectionIds": [
"6c0ba537-a96b-4d74-8c95-450eb88baee8"
],
"transformations": [
{
"name": "Mapping",
"params": {
"mappingId": "22922102bffd4369b6209c102a604062",
"mappingVersion": 0
}
}
],
"scheduleParams": {
"startTime": "1604961070",
"frequency": "once"
}
}'
flowSpec.idsourceConnectionIdstargetConnectionIdstransformations.params.mappingIdtransformations.params.mappingIdscheduleParams.startTimescheduleParams.frequencyfrequency at which the dataflow will collect data. Acceptable values include: once, minute, hour, day, or week.scheduleParams.intervalThe interval designates the period between two consecutive flow runs. The interval’s value should be a non-zero integer. The minimum accepted interval value for each frequency is as follows:
- Once: n/a
- Minute: 15
- Hour: 1
- Day: 1
- Week: 1
Response
A successful response returns the ID id of the newly created dataflow.
{
"id": "20c115bc-46e3-40f3-bfe9-fb25abe4ba76",
"etag": "\"030018cb-0000-0200-0000-5fa9c31a0000\""
}
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 more information on how to monitor dataflows, see the tutorial on monitoring dataflows in the API
Next steps
By following this tutorial, you have created a source connector to collect data e-commerce on a scheduled basis. Incoming data can now be used by downstream Platform services such as Real-Time Customer Profile and Data Science Workspace. See the following documents for more details: