Connect Pointagram and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Pointagram
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Pointagram and TimescaleDB
Enable Integrations or automations with these events of Pointagram and TimescaleDB
Actions
Find Players
Looks up a player by matching either player name, player external id or email-address.
Create Player
Creates a new player.
Create or Find Player
Search for an existing player or create a new one if the player is not found.
Add Points
Add points for a player in a score series
Update a Player
Updates an existing player
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Frequently Asked Questions
To start, connect both your Pointagram and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Pointagram triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Pointagram 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 Pointagram 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 Pointagram 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 Pointagram
Pointagram is a platform designed to gamify team activities and enhance engagement through point-based challenges and rewards. It allows teams to create competitions, track progress, and celebrate achievements, fostering a fun and motivating environment.
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.
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