
When this happens...
Finished Actor Run
Finished Task Run

Automatically do this!
Enable Integrations or automations with these events of Apify and TimescaleDB
Triggers whenever a selected actor is run and finished.
Triggers whenever a selected task is run and finished.
Run a selected actor task.
Creates a new or updates an existing record in a key-value store.
Runs a selected actor.
Finds the most recent actor run with a specific status.
Find a dataset in Apify by searching with its ID.
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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 Apify and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Apify triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Apify 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 Apify 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 Apify 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.
Apify is a powerful, scalable platform designed to automate web scraping, data extraction, and web automation tasks effortlessly. Whether you need to collect data from websites, automate repetitive browser actions, or build complex workflows, Apify provides a robust, developer-friendly environment to make it happen—without sacrificing flexibility or control.
Learn MoreTimescaleDB 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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