Enable Integrations or automations with these events of PayHere and KNIME
Triggers when a payment is received.
Triggers when a user has cancelled their subscription, or Stripe has marked it as cancelled due to payment failures.
Creates a new payment plan
Update the company information for currently authenticated user.
Issue a refund a customer the amount specified, can be a partial refund.
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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 PayHere and KNIME accounts to viaSocket. Once connected, you can set up a workflow where an event in PayHere triggers actions in KNIME (or vice versa).
Absolutely. You can customize how PayHere data is recorded in KNIME. This includes choosing which data fields go into which fields of KNIME, setting up custom formats, and filtering out unwanted information.
The data sync between PayHere and KNIME 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 PayHere and KNIME. 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.
PayHere is a comprehensive online payment gateway service that facilitates seamless and secure transactions for businesses and individuals in Sri Lanka. It supports multiple payment methods, including credit cards, mobile payments, and bank transfers, making it easier for users to manage their financial transactions online.
Learn MoreKNIME is a powerful open-source platform designed for data analytics, reporting, and integration. It enables users to create data workflows and perform complex data analysis with ease, using a visual programming interface. KNIME is widely used for data mining, machine learning, and predictive analytics, making it a valuable tool for data scientists and analysts.
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