From Data Warehouse to Data Service: A Modern Approach with Searches.do

Your organization's most valuable asset is locked away. It sits in data warehouses, sprawls across NoSQL databases, and is fragmented behind dozens of microservice APIs. Accessing it often requires writing complex, one-off SQL queries or building tightly-coupled integrations that are brittle, hard to maintain, and slow to ship.

What if you could treat data retrieval not as a query, but as a service? A clean, reusable, and intelligent service that decouples your applications from the complexity of the underlying data sources.

This is the shift from the data warehouse to the data service—a modern paradigm that transforms how you build, manage, and consume data. And with Searches.do, this transformation is simpler than ever.

The Old Way: The Tyranny of the Ad-Hoc Query

For decades, getting data meant going directly to the source. This traditional approach comes with a familiar set of problems:

• Brittleness: A developer writes a 100-line SQL query with five joins for a new feature. A month later, a column in one of those tables is renamed, and the entire feature breaks silently.
• Duplication: The sales dashboard needs a list of "active users." The marketing automation tool needs the same list. The support portal needs it too. Soon, you have three slightly different, independently-maintained queries all trying to define the same core business concept.
• Security Gaps: Giving direct database access to multiple applications and services is risky. Managing credentials and fine-grained permissions becomes a nightmare, increasing the surface area for potential breaches.
• Knowledge Silos: The one person who understands the intricate relationships in the data warehouse becomes a bottleneck. If they're unavailable, development grinds to a halt.

This model doesn't scale. It creates technical debt, slows down development cycles, and keeps valuable data siloed from the people and applications that need it most.

The New Way: Data as a Service with Search Agents

The "Data as a Service" model proposes a simple, powerful idea: Encapsulate complex data retrieval logic into a standalone, reusable API.

Instead of every application querying the database directly, they call a well-defined endpoint. For example, instead of writing SQL to find a user, you simply call a findUserByEmail service.

This is where Searches.do comes in. It provides an agentic workflow platform to build, deploy, and manage these data services, which we call Search Agents.

A Search Agent is a piece of code that defines three things:

1. What it does (e.g., "Find User By Email").
2. What inputs it needs (e.g., an email string).
3. How it accomplishes the task (the actual logic to query a database, call another API, or even combine multiple sources).

Let's see it in action.

Building Your First Data Service in Under a Minute

With Searches.do, you define your data service as code. This "Business as Code" approach makes your logic versionable, testable, and easy to manage.

Here’s how you'd create a findUserByEmail Search Agent:

import { Search } from 'searches.do';

const findUserByEmail = new Search({
  name: 'Find User By Email',
  description: 'Retrieves a single user record by their email address.',
  parameters: {
    email: { type: 'string', required: true }
  },
  handler: async ({ email }) => {
    // Your complex data retrieval logic is safely encapsulated here
    // It could be a SQL query, a NoSQL lookup, or an internal API call
    const user = await db.collection('users').findOne({ email });
    return user;
  }
});

// Searches.do automatically deploys this as a secure, scalable API endpoint:
// POST /findUserByEmail
// { "email": "jane.doe@example.com" }

Let's break down the magic:

• parameters: This defines your clean API contract. No more messy query strings. Your API is self-documenting and predictable.
• handler: This is your black box. You can put any logic you want in here—query a Snowflake data warehouse, a PostgreSQL database, or even a third-party service like Stripe. The consumer of the API doesn't need to know or care about the implementation details.
• Instant Deployment: Once defined, Searches.do instantly deploys this agent as a robust API endpoint. No need to configure servers, set up gateways, or manage infrastructure.

The Benefits of an Agentic Search Platform

By embracing this model, you unlock powerful advantages across your entire tech stack.

1. Radical Decoupling: Your frontend applications and other services are now completely isolated from the underlying data architecture. You can migrate your entire database from MongoDB to Postgres, and as long as you update the logic in the handler, not a single client application needs to change.

2. Centralized Business Logic: The definition of an "active user" or a "qualified lead" now lives in exactly one place—its dedicated Search Agent. This ensures consistency and creates a single source of truth for your core business concepts.

3. Enhanced Security & Governance: Instead of passing database credentials around, applications are given access only to the specific Search Agents they need. Every call is managed and can be audited.

4. From Data Retrieval to Intelligent Agents: The real power of Searches.do is that the handler is just code. This means you can integrate Large Language Models (LLMs) to create truly "agentic" search experiences. A user could ask, "show me our top 10 customers in France last quarter," and the agent can parse this natural language request, formulate the right query, and return the data.

Unlock Your Data's True Potential

Stop writing one-off queries. Start building a library of reusable, intelligent data services. Searches.do provides the framework to move beyond the limitations of the traditional data warehouse and embrace a more agile, scalable, and powerful future.

Transform your complex data retrieval logic into simple, reusable APIs. Your data, unlocked.

Ready to build your first Search Agent? Visit Searches.do to get started.