A Practical Guide to Building a Scalable Data Enrichment Pipeline

In today's data-driven world, the richness of your user data can be a significant competitive advantage. Combining internal data with third-party sources like Clearbit or LinkedIn can unlock powerful insights for sales, marketing, and product development. However, building and maintaining the infrastructure to perform this data enrichment is often a complex and brittle engineering challenge.

You have to manage multiple API integrations, handle authentication, respect rate limits, implement retry logic, and scale your infrastructure to handle fluctuating loads. What starts as a simple script can quickly balloon into a DevOps nightmare.

What if you could offload all that complexity? What if you could define your entire data enrichment logic as code and execute it as a single, reliable, and scalable API call?

This is the promise of processing.services.do. In this guide, we'll walk you through how to build a powerful data enrichment pipeline using an agentic workflow platform, transforming a complex process into a simple, manageable service.

The Challenge with Traditional Data Processing

Imagine you need to build a service that enriches a new user profile. Your goal is to collect information from three sources:

1. Your internal user database.
2. The Clearbit API for firmographic data.
3. A LinkedIn service for professional details.

A traditional approach might involve a monolithic service or a series of serverless functions glued together. You’d immediately face several challenges:

• Orchestration: How do you run these tasks in parallel to minimize latency?
• Error Handling: What happens if the Clearbit API is temporarily down? Do you retry? Does the whole job fail?
• State Management: How do you track the progress of a long-running enrichment job for a million users?
• Scalability: How do you ensure your system can handle a sudden influx of 100,000 new users without falling over or breaking the bank?

This is where you spend most of your time—on the plumbing, not the business logic.

A Better Way: Agentic Workflows for Intelligent Automation

processing.services.do offers a different paradigm. Instead of building the plumbing, you focus on the core business logic. This is achieved through agentic workflows—a system where you define your logic as independent, containerized "agents," and the platform handles the orchestration, execution, and scaling.

Think of it this way:

• You define the "what": Small, focused pieces of code (agents) that do one thing well, like calling a specific API.
• The platform handles the "how": It invokes your agents, manages dependencies, scales resources up and down, handles errors, and ensures the job gets done.

Tutorial: Building Your Enrichment Pipeline in 4 Steps

Let's build our scalable data enrichment pipeline.

Step 1: Define Your Logic as Containerized Agents

First, you package each piece of your business logic into a containerized agent. An agent is just a stateless application that accepts a payload, performs a task, and returns a result.

For our workflow, you would create three agents:

1. enrich-clearbit-agent: A container that takes a user ID, finds an associated email address, and calls the Clearbit Enrichment API.
2. enrich-linkedin-agent: A container that uses a user ID to query a LinkedIn data source.
3. enrich-internal-db-agent: A container that connects to your internal database and retrieves user records.

By isolating the logic, each agent becomes simple to build, test, and maintain. You're no longer managing a distributed system, just writing focused functions.

Step 2: Define the Orchestration Workflow

Once your agents are defined and registered on the processing.services.do platform, you define a workflow. This is a simple declaration (e.g., a YAML or JSON file) that tells the platform how to orchestrate your agents.

You might define a workflow named enrich-user-profile that specifies: "When triggered with a userId and a list of sources, run the corresponding agents in parallel and aggregate their results."

We can visualize this flow:

graph TD
    A[API Call to processing.services.do] --> B{Workflow: enrich-user-profile};
    B --> C[enrich-clearbit-agent];
    B --> D[enrich-linkedin-agent];
    B --> E[enrich-internal-db-agent];
    C --> F{Aggregate Results};
    D --> F;
    E --> F;
    F --> G[POST to Webhook];

The platform's orchestrator reads this definition and handles the parallel execution automatically. This is the core of true workflow automation.

Step 3: Trigger Your Workflow with a Simple API Call

Now for the best part. With the complex logic defined and orchestrated on the platform, triggering it is incredibly simple. All the complexity is hidden behind a clean API service.

Using the platform's SDK, you can kick off a high-priority job that will notify you via a webhook upon completion.

import { Do } from '@do-sdk/core';

const processing = new Do('processing.services.do', {
  apiKey: 'your-api-key',
});

// Define and run a data enrichment workflow
const job = await processing.run({
  workflow: 'enrich-user-profile',
  payload: {
    userId: 'usr_12345',
    sources: ['clearbit', 'linkedin', 'internal_db'],
  },
  config: {
    priority: 'high',
    onComplete: 'https://myservice.com/webhook/job-done',
  }
});

console.log(`Job started with ID: ${job.id}`);

That's it. Your application sends one secure API request. The processing.services.do platform takes over, spinning up the necessary resources to execute your agents in parallel, and your application can move on.

Step 4: Receive Results Asynchronously

Because data enrichment can be time-consuming, the platform is designed for asynchronous operations. When the job is finished, the platform will gather the results from all the agents and send a single POST request to the onComplete webhook you specified.

The payload of this webhook might look something like this:

{
  "jobId": "job_abc123",
  "status": "completed",
  "result": {
    "clearbit": { "company": "Acme Corp", "employees": 500 },
    "linkedin": { "title": "Software Engineer", "connections": "500+" },
    "internal_db": { "signup_date": "2023-01-15", "plan": "enterprise" }
  }
}

Your service simply needs one endpoint to listen for these results, completely decoupling it from the execution of the enrichment workflow.

Why This Approach Delivers Scalable Processing

Building your data pipelines this way provides immense benefits:

• Simplicity: Your application code is reduced to a single API call. The data processing logic is neatly organized into independent, maintainable agents.
• Scalability: Whether you need to process one user or one million, the platform handles it. It automatically scales compute resources to meet demand, ensuring high-throughput, parallel processing without any manual intervention.
• Resilience: The platform manages retries and error handling. A temporary failure in one source won't bring down your entire system.
• Focus: Your developers can focus on writing business logic that delivers value, not on building and maintaining complex distributed systems infrastructure.

Frequently Asked Questions (FAQ)

Q: What kind of processing can I perform with processing.services.do?
A: You can run virtually any custom logic. Common use cases include data transformation (ETL), batch processing, image/video rendering, financial calculations, and orchestrating sequences of microservice calls. If you can code it, we can process it.

Q: How do I define my processing logic?
A: You define your business logic as containerized agents. processing.services.do acts as the orchestrator, invoking your agents with the provided payload and managing the execution state, scalability, and error handling for you.

Q: Is the processing service scalable?
A: Yes. Our platform is engineered for high-throughput, parallel processing. It automatically scales compute resources based on your workload, ensuring your jobs are completed efficiently, whether you're running one task or a million.

Q: Can I run long-running tasks?
A: Absolutely. The platform is designed for both synchronous (quick) and asynchronous (long-running) jobs. For long jobs, you can provide a webhook URL to be notified upon completion, allowing you to build robust, event-driven systems.

Get Started with Intelligent Processing Today

Ready to stop wrestling with complex data pipelines and start leveraging simple, scalable workflow automation? Transform your most intricate processes into on-demand API calls.

Visit processing.services.do to learn more and start building for free.