Connect Google Bigquery and AI/ML API to Build Intelligent Automations
Enable Integrations or automations with these events of Google Bigquery and AI/ML API
Enable Integrations or automations with these events of Google Bigquery and AI/ML API
Actions
Delete Rows
Deletes rows from a BigQuery table that match the provided WHERE condition.
Run SQL query
Run a SQL query and return results.
Find Row
Find row(s) by specifying a table, a WHERE clause, an optional ORDER BY, and a LIMIT.
Insert Rows
Insert one or more rows into a BigQuery table.
Update Rows
Update specified fields in rows of a BigQuery table that match the provided WHERE condition.
Create a Table
Creates a new, empty table in the dataset.
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Frequently Asked Questions
To start, connect both your Google Bigquery and AI/ML API accounts to viaSocket. Once connected, you can set up a workflow where an event in Google Bigquery triggers actions in AI/ML API (or vice versa).
Absolutely. You can customize how Google Bigquery data is recorded in AI/ML API. This includes choosing which data fields go into which fields of AI/ML API, setting up custom formats, and filtering out unwanted information.
The data sync between Google Bigquery and AI/ML API 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 Google Bigquery and AI/ML API. 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 Google Bigquery
BigQuery is Google's serverless and highly scalable enterprise data warehouse, designed to make all your data analysts productive.
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About AI/ML API
AI/ML API provides advanced artificial intelligence and machine learning capabilities to developers and businesses, enabling them to integrate intelligent features into their applications. With a wide range of AI models and tools, users can enhance their software with capabilities such as natural language processing, image recognition, predictive analytics, and more.
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