Your data warehouse is the heart of your business intelligence. Platforms like Snowflake, BigQuery, and Postgres are powerful repositories of clean, transformed, and valuable data, holding a complete picture of your customers, products, and operations. But for many organizations, this data remains passive—a resource to be queried for reports and dashboards, but not a driver of immediate action.
What if you could bridge the gap between insight and execution? What if a change in your data warehouse—a customer reaching VIP status, inventory dropping below a threshold, or a lead's score crossing a certain point—could instantly and reliably trigger a business process?
This is the power of connecting your data warehouse to an agentic workflow engine like .do. By treating your data as a real-time event stream, you can unlock its operational value and build a truly data-driven, automated business.
The traditional flow of data involves periodic analysis, manual decision-making, and then, finally, action. This latency means missed opportunities, from delayed customer engagement to slow operational responses.
By connecting your data warehouse directly to .do, you can build workflows that are:
The core idea is simple: monitor your data warehouse for specific conditions, and when those conditions are met, make an API call to execute a .do action or workflow. There are a few common patterns to achieve this.
This is the most straightforward and widely applicable method.
Here’s a conceptual example of a scheduled job processing new users:
import { Do } from '@do-sdk/core';
// This function would be invoked by a scheduler
async function processNewUsers() {
const newUsers = await database.query(
"SELECT user_id FROM users WHERE status = 'needs_onboarding'"
);
if (!newUsers.length) {
console.log('No new users to process.');
return;
}
const a = new Do(process.env.DO_API_KEY);
for (const user of newUsers) {
console.log(`Executing onboarding for ${user.user_id}`);
// Execute a specific, atomic action
const { error } = await a.action.execute({
name: 'send-onboarding-email',
params: {
userId: user.user_id,
template: 'new-user-onboarding-v3'
},
// Important: Use an idempotency key to prevent duplicates
idempotencyKey: `onboard-email-${user.user_id}`
});
if (error) {
console.error('Action failed:', error);
} else {
// Mark user as processed to avoid re-triggering
await database.query(
"UPDATE users SET status = 'onboarding_initiated' WHERE user_id = ?",
[user.user_id]
);
}
}
}
For use cases that demand near-instantaneous response, Change Data Capture (CDC) is the ideal solution. Tools like Debezium (for Postgres) or native features like Snowflake Streams can emit an event every time a row is inserted, updated, or deleted.
This pattern is perfect for transactional processes like "order confirmed," "shipment created," or "payment failed," where immediate follow-up is critical.
Connecting your data warehouse to your operational systems introduces powerful capabilities, but it also requires a new level of reliability. This is where action.do shines.
Your data warehouse holds the key to incredible operational efficiency and proactive customer engagement. By treating it as a source of real-time events, you can activate its value in ways that dashboards and reports alone never can.
By building on a foundation of reliable, auditable, and idempotent atomic actions with .do, you can confidently build the next generation of automated business processes.
Ready to turn your data into action? Explore how action.do provides the fundamental building blocks for your data-driven agentic workflows.