Connect Google Bigquery and kazm to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Google Bigquery
Choose an Action
Automatically do this!
- Request a new Action for kazm
Enable Integrations or automations with these events of Google Bigquery and kazm
Enable Integrations or automations with these events of Google Bigquery and kazm
Actions
Delete Rows
Deletes rows in a Google BigQuery table that match the condition you provide.
Run SQL Query
Run a SQL query on Google BigQuery and return the results.
Insert Rows
Adds one or more rows to a BigQuery table.
Update Rows
Change specific fields for rows in a BigQuery table that match the rules you provide.
Create Table
Creates a new, empty table in a BigQuery dataset.
List BigQuery Projects
Lists the BigQuery projects you have access to.
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Frequently Asked Questions
To start, connect both your Google Bigquery and kazm accounts to viaSocket. Once connected, you can set up a workflow where an event in Google Bigquery triggers actions in kazm (or vice versa).
Absolutely. You can customize how Google Bigquery data is recorded in kazm. This includes choosing which data fields go into which fields of kazm, setting up custom formats, and filtering out unwanted information.
The data sync between Google Bigquery and kazm 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 kazm. 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.
Learn MoreAbout kazm
Kazm is the data-driven CRM for Web3. We plug into every community touchpoint to create unified customer profiles, then apply machine learning and other methods to label, segment, and help personalize experiences for users.
Learn More
