Connect workable and TimescaleDB to Build Intelligent Automations

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workable

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TimescaleDB

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Enable Integrations or automations with these events of workable and TimescaleDB

Enable Integrations or automations with these events of workable and TimescaleDB

Actions

Create Department

Create Department

Creates a Department

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Frequently Asked Questions

How do I start an integration between workable and TimescaleDB?

To start, connect both your workable and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in workable triggers actions in TimescaleDB (or vice versa).

Can we customize how data from workable is recorded in TimescaleDB?

Absolutely. You can customize how workable 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.

How often does the data sync between workable and TimescaleDB?

The data sync between workable and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.

Can I filter or transform data before sending it from workable to TimescaleDB?

Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.

Is it possible to add conditions to the integration between workable and TimescaleDB?

Yes, you can set conditional logic to control the flow of data between workable 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.

workable

About workable

Workable is a beautifully simple tool that helps you advertise jobs, screen candidates and accelerate your company's hiring process.

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TimescaleDB

About TimescaleDB

TimescaleDB 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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