Enable Integrations or automations with these events of Adyen and TimescaleDB
Create an Adyen checkout session and return the session ID, expiry, and data for redirect or embedded checkout.
Create a new Adyen checkout order to start payment processing and return order details (provider reference, order reference, amount, expiry).
Cancel an existing Adyen order to stop further processing and, when supported, release reserved funds.
Generate an Adyen-hosted payment link for a specified amount and shopper so they can complete payment.
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Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your Adyen and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Adyen triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Adyen data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between Adyen and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Adyen and TimescaleDB. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
Adyen is a global payment platform that enables businesses to accept payments online, in-app, and in-store through a single solution. With advanced fraud protection, seamless checkout experiences, and support for major payment methods worldwide, Adyen powers secure and scalable payment processing for leading enterprises.
Learn MoreTimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
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