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In the workspace, click the βCreate New Scenarioβ button.

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

Select the Databricks node from the app selection panel on the right.

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

Databricks
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Data Enrichment
Now, click the Data Enrichment node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Data Enrichment settings. Authentication allows you to use Data Enrichment through Latenode.
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Use various Latenode nodes to transform data and enhance your integration:

JavaScript
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AI Anthropic Claude 3
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Data Enrichment
Trigger on Webhook
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Databricks
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β
Iterator
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Webhook response
After configuring Databricks, Data Enrichment, and any additional nodes, donβt 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.
Run the scenario by clicking βRun onceβ and triggering an event to check if the Databricks and Data Enrichment integration works as expected. Depending on your setup, data should flow between Databricks and Data Enrichment (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Airtable + Data Enrichment + Databricks: When new records are added to Airtable, enrich customer profiles using Data Enrichment. Then, trigger a Databricks job run to analyze the updated customer data.
Google Sheets + Data Enrichment + Databricks: When a new row is added to Google Sheets, lead data is enriched using Data Enrichment. The enriched data is then queried in Databricks using SQL Warehouse to score and analyze for lead quality.
About Databricks
Use Databricks inside Latenode to automate data processing pipelines. Trigger Databricks jobs based on events, then route insights directly into your workflows for reporting or actions. Streamline big data tasks with visual flows, custom JavaScript, and Latenode's scalable execution engine.
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About Data Enrichment
Enrich lead data, verify addresses, or flag fraud risks within Latenode workflows. Connect Data Enrichment APIs to auto-update records across apps. Streamline data cleaning and validation with no-code blocks or custom JS. Automate tasks that need enhanced data for better decisions, at scale.
Related categories
How can I connect my Databricks account to Data Enrichment using Latenode?
To connect your Databricks account to Data Enrichment on Latenode, follow these steps:
Can I enrich Databricks data with external sources?
Yes! Latenode's integration enables seamless enrichment. Enhance Databricks data quality by automatically adding data from diverse external sources, improving analytics and insights.
What types of tasks can I perform by integrating Databricks with Data Enrichment?
Integrating Databricks with Data Enrichment allows you to perform various tasks, including:
How does Latenode enhance Databricks data processing?
Latenode adds no-code logic, AI, and JavaScript to Databricks. Automate complex pipelines and transform data with minimal coding needed.
Are there any limitations to the Databricks and Data Enrichment integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of: