Adyen connector

Use this as an implementation starting point: adapt the worked example to the requested dataset and the source documentation. See Custom API for the shared SDK pattern and document-upload workflow.

What this connector is forCopy Anchor Link

Payments platform for transactions, settlements, and payment operations.

Use this guide to choose the resources and row meanings for a Vortexify source integration. Connection ownership belongs to a connector; each dataset gets its own sync, configuration, and extraction behavior.

Data and tables you can pullCopy Anchor Link

The following table families are a starting point for source selection. They describe possible datasets to implement using the vendor reference, not a guarantee that the example implements every table. Actual availability depends on the product edition, deployment, account permissions, and API/schema version.

Relationships and extraction considerationsCopy Anchor Link

These are proposed dataset names, not a universal schema guaranteed by the vendor. Preserve source object IDs and relationship references, and split nested child collections into separate tables when appropriate. Choose the concrete endpoint/report and row meaning from the vendor reference before implementing a sync.

Connecting to AdyenCopy Anchor Link

Use reporting credentials for report downloads, distinct from payment-creation access. Choose the report and settlement/account scope, generate or locate it, and parse its rows. A generic payment request does not enumerate historical transactions.

For HTTP reads, configure the connector client with the concrete API origin and source-authorized headers or authentication object. Actual credentials belong in platform secret configuration, not in this document or committed source.

requirements.inCopy Anchor Link

Declare these packages in the resource's requirements.in. Use the published vortexify package for the SDK and the source-specific driver/client shown below. Choose compatible dependency versions for your runtime; the connector and sync reference explains where each file belongs.

vortexify==0.1.1
pandas>=2,<3
httpx

This HTTP starting point needs no native database driver. Add the source-specific SDK, signer, or XML parser if the selected interface requires it.

Worked example: prepared CSV report batchesCopy Anchor Link

This source-read pattern shows how to shape the selected resource into DataFrames. Use it inside the dataset sync, retaining shared connection ownership in the connector. Its scope is the named resource and filters; adapt the projection, batching, and preview to the requested table.

Source readCopy Anchor Link

from tempfile import TemporaryFile
import pandas as pd


def read_batches(client, report_path):
    # report_path identifies a prepared CSV report on this authorized source host.
    # Generate/locate it through the selected vendor reporting workflow first.
    with TemporaryFile(mode="w+b") as local_file:
        with client.stream("GET", report_path) as response:
            response.raise_for_status()
            for chunk in response.iter_bytes():
                local_file.write(chunk)
        local_file.seek(0)
        for frame in pd.read_csv(local_file, chunksize=1000):
            yield frame

What to adaptCopy Anchor Link

Download an authorized prepared report and parse it in bounded row batches. This stages the file on disk and does not generate a report or discover every report. Select the exact report URL/path, credentials, time bounds, CSV dialect, and schema from the vendor reporting documentation. Disk space must accommodate the report.

Use the custom API SDK starting point to connect the read operation to Connector and Sync. For a source with its own driver, retain that client in Connector.client() and replace the HTTP request with the driver read. An extract(*, avg_row_size_bytes=None) implementation yields bounded DataFrames. An independent preview() uses a fresh connector and a small read; it must not call extraction or advance real state.

Choose explicit output columns and stable IDs for the requested table. The examples use json_normalize where useful to illustrate row shaping; nested arrays still need deliberate child tables and a stable declared output schema. A first-page sample is useful for development, while a full sync must follow all relevant continuation/parent-resource paths. Design incremental watermarks, update/deletion behavior, and rate-limit handling for the actual source before enabling incremental mode.

The purpose is to give a coding agent a concrete pattern to adapt, including for similar sources. These snippets are examples rather than a claim of complete vendor coverage. Check the source operation reference and representative responses before running with a customer's account; live vendor execution has not been verified here.

Sources and further readingCopy Anchor Link

• Vendor connection and resource reference — The interface/access prerequisites underlying this connection path; follow the selected resource's documentation for fields, queries, and pagination.
• Vortexify connectors and syncs — Connector ownership, schemas, preview isolation, extraction, and checkpoint contracts.
• Vortexify CLI workflow — Registering resources, committing source, and providing credential configuration.