Connect Teamtailor and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Teamtailor
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Teamtailor and TimescaleDB
Enable Integrations or automations with these events of Teamtailor and TimescaleDB
Actions
Create Note
Creates a new note on a candidate.
Update Candidate
Updates an already existing candidate.
Update Job
Updates an already existing candidate.
Find Candidate
Finds a candidate by email.
Create Job
Creates a New Job
Create Candidate
Create a new Candidate
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Frequently Asked Questions
To start, connect both your Teamtailor and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Teamtailor triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Teamtailor 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 Teamtailor 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 Teamtailor 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.
About Teamtailor
Teamtailor is a recruitment and employer branding ATS (Applicant Tracking System) designed to assist teams of all sizes to attract and hire talent efficiently and effectively. The platform integrates various capabilities such as job posting, applicant tracking, and candidate communication.
Learn MoreAbout 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.
Learn More
