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How to connect Google Cloud BigQuery and Airparser
Create a New Scenario to Connect Google Cloud BigQuery and Airparser
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, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Cloud BigQuery or Airparser will be your first step. To do this, click "Choose an app," find Google Cloud BigQuery or Airparser, and select the appropriate trigger to start the scenario.
Add the Google Cloud BigQuery Node
Select the Google Cloud BigQuery node from the app selection panel on the right.
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Configure the Google Cloud BigQuery
Click on the Google Cloud BigQuery node to configure it. You can modify the Google Cloud BigQuery URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Airparser Node
Next, click the plus (+) icon on the Google Cloud BigQuery node, select Airparser from the list of available apps, and choose the action you need from the list of nodes within Airparser.
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Authenticate Airparser
Now, click the Airparser node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Airparser settings. Authentication allows you to use Airparser through Latenode.
Configure the Google Cloud BigQuery and Airparser 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 and Airparser 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.
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Save and Activate the Scenario
After configuring Google Cloud BigQuery, Airparser, 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 and Airparser integration works as expected. Depending on your setup, data should flow between Google Cloud BigQuery and Airparser (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 and Airparser
Airparser + Google Sheets + Slack: When a new document is parsed by Airparser, the extracted data is added as a new row in Google Sheets. Then, a summary of the added data is sent to a Slack channel.
Google Sheets + Airparser + Slack: When a new row is added to Google Sheets, Airparser gets a specific document and the extracted data is checked. If a certain condition applies, Slack sends an alert to a specified channel.
Google Cloud BigQuery and Airparser integration alternatives
About Google Cloud BigQuery
Use Google Cloud BigQuery in Latenode to automate data warehousing tasks. Query, analyze, and transform huge datasets as part of your workflows. Schedule data imports, trigger reports, or feed insights into other apps. Automate complex analysis without code and scale your insights with Latenode’s flexible, pay-as-you-go platform.
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About Airparser
Airparser in Latenode extracts data from PDFs, emails, and documents. Automate data entry by feeding parsed content directly into your CRM or database. Use Latenode's logic functions to validate or transform data, then trigger actions like sending notifications or updating records. Scale document processing without complex code.
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FAQ Google Cloud BigQuery and Airparser
How can I connect my Google Cloud BigQuery account to Airparser using Latenode?
To connect your Google Cloud BigQuery account to Airparser on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Cloud BigQuery and click on "Connect".
- Authenticate your Google Cloud BigQuery and Airparser accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automatically update BigQuery tables with parsed Airparser data?
Yes, using Latenode! Automatically updating BigQuery with parsed data from Airparser saves time, eliminates manual entry, and improves data accuracy via scheduled workflows and real-time triggers.
What types of tasks can I perform by integrating Google Cloud BigQuery with Airparser?
Integrating Google Cloud BigQuery with Airparser allows you to perform various tasks, including:
- Extracting data from emails and inserting it into BigQuery datasets.
- Parsing invoices from email attachments and storing totals in BigQuery.
- Analyzing customer feedback from surveys parsed by Airparser in BigQuery.
- Tracking marketing campaign performance by combining data sources.
- Building custom dashboards using parsed data stored securely in BigQuery.
What data types are supported for BigQuery integrations in Latenode?
Latenode supports all standard BigQuery data types, ensuring seamless data transfer and transformation within your automated workflows. You also have access to advanced data manipulation via JavaScript.
Are there any limitations to the Google Cloud BigQuery and Airparser integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Data transfer speeds depend on the size of the data and network conditions.
- Complex data transformations may require JavaScript knowledge.
- Airparser document parsing limitations may affect extracted data quality.
