How to connect Google Cloud BigQuery (REST) and Slack
Create a New Scenario to Connect Google Cloud BigQuery (REST) and Slack
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Google Cloud BigQuery (REST), triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud BigQuery (REST) or Slack will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery (REST) or Slack, and select the appropriate trigger to start the scenario.

Add the Google Cloud BigQuery (REST) Node
Select the Google Cloud BigQuery (REST) node from the app selection panel on the right.

Google Cloud BigQuery (REST)
Configure the Google Cloud BigQuery (REST)
Click on the Google Cloud BigQuery (REST) node to configure it. You can modify the Google Cloud BigQuery (REST) URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Slack Node
Next, click the plus (+) icon on the Google Cloud BigQuery (REST) node, select Slack from the list of available apps, and choose the action you need from the list of nodes within Slack.

Google Cloud BigQuery (REST)
⚙

Slack

Authenticate Slack
Now, click the Slack node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Slack settings. Authentication allows you to use Slack through Latenode.
Configure the Google Cloud BigQuery (REST) and Slack Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Google Cloud BigQuery (REST) and Slack Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

JavaScript
⚙
AI Anthropic Claude 3
⚙

Slack
Trigger on Webhook
⚙
Google Cloud BigQuery (REST)
⚙
⚙
Iterator
⚙
Webhook response

Save and Activate the Scenario
After configuring Google Cloud BigQuery (REST), Slack, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Google Cloud BigQuery (REST) and Slack integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery (REST) and Slack (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Google Cloud BigQuery (REST) and Slack
Google Cloud BigQuery (REST) + Slack + Google Sheets: Analyze data in Google Cloud BigQuery using a REST query, post the key findings to a designated Slack channel, and log a summary of the analysis, including the query used and the results, in a Google Sheet for record-keeping and future reference.
Slack + Google Cloud BigQuery (REST) + Jira: Monitor a Slack channel for messages containing a specific Jira issue key. When an issue is mentioned, query Google Cloud BigQuery for related data, such as logs or metrics associated with that issue, and then update the corresponding Jira ticket with the findings.
Google Cloud BigQuery (REST) and Slack integration alternatives
About Google Cloud BigQuery (REST)
Automate BigQuery data workflows in Latenode. Query and analyze massive datasets directly within your automation scenarios, bypassing manual SQL. Schedule queries, transform results with JavaScript, and pipe data to other apps. Scale your data processing without complex coding or expensive per-operation fees. Perfect for reporting, analytics, and data warehousing automation.
Similar apps
Related categories

About Slack
Send Slack messages and automate channel updates directly from Latenode workflows. Get instant alerts on critical events, share data insights, or trigger actions based on user input. Centralize notifications and approvals by combining Slack with databases, CRMs, and AI models within a scalable, low-code environment.
Related categories
See how Latenode works
FAQ Google Cloud BigQuery (REST) and Slack
How can I connect my Google Cloud BigQuery (REST) account to Slack using Latenode?
To connect your Google Cloud BigQuery (REST) account to Slack on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud BigQuery (REST) and click on "Connect".
- Authenticate your Google Cloud BigQuery (REST) and Slack accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I get alerts in Slack on new BigQuery data?
Yes, you can! Latenode lets you set up automated Slack alerts based on new Google Cloud BigQuery (REST) data. Get real-time insights and react faster using custom workflows.
What types of tasks can I perform by integrating Google Cloud BigQuery (REST) with Slack?
Integrating Google Cloud BigQuery (REST) with Slack allows you to perform various tasks, including:
- Send daily sales reports from BigQuery to a Slack channel.
- Post alerts to Slack when specific data thresholds are reached.
- Share customer feedback analysis from BigQuery in a Slack thread.
- Automatically update project status in Slack based on BigQuery data.
- Create a searchable archive of BigQuery data directly within Slack.
How do I handle large datasets from BigQuery in Latenode?
Latenode's robust architecture efficiently handles large datasets from Google Cloud BigQuery (REST). Use our built-in tools and JavaScript blocks for complex processing.
Are there any limitations to the Google Cloud BigQuery (REST) and Slack integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Initial setup requires valid credentials for both services.
- Complex queries may require some familiarity with SQL.
- Rate limits of both Google Cloud BigQuery (REST) and Slack apply.