Connect Planado and TimescaleDB to Build Intelligent Automations

Choose a Trigger

Planado

When this happens...

Choose an Action

TimescaleDB

Automatically do this!

Enable Integrations or automations with these events of Planado and TimescaleDB

Enable Integrations or automations with these events of Planado and TimescaleDB

Actions

Create Job

Create Job

Create a new job assignment for a field worker

Request a new Action for Planado

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Frequently Asked Questions

How do I start an integration between Planado and TimescaleDB?

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

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

Absolutely. You can customize how Planado 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 Planado and TimescaleDB?

The data sync between Planado 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 Planado 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 Planado and TimescaleDB?

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

Planado

About Planado

Planado is a cloud service for the company’s fieldwork management and performance quality control of mobile workers

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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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