Business-as-Code: How to Version-Control Your Data-Retrieval Logic

In the world of software development, we live by the gospel of version control. We use Git for every line of application code, every piece of infrastructure configuration, and every CI/CD pipeline. But there's a critical piece of our technology stack that often gets left behind in this disciplined world: our data retrieval logic.

These are the complex SQL queries, the NoSQL aggregation pipelines, and the one-off scripts that represent our core business rules. They live in scattered .sql files, buried deep within application code, or worse, only on a data analyst's local machine. They are fragile, difficult to find, impossible to reuse, and a nightmare to update.

What if we treated this vital logic with the same respect as our application code? This is the core idea behind Business-as-Code (BaC)—and it's time to apply it to how we access our data.

The Chaos of Traditional Data Retrieval

If you've ever had to hunt down a query responsible for a critical business report, you know the pain. The traditional approach to data retrieval is fundamentally broken and leads to several recurring problems:

• The Scattered Script Graveyard: Your business logic is fragmented across dozens of services, scripts, and BI tools. There's no single source of truth for how to find "all active users in the EU" or calculate "monthly recurring revenue."
• Zero Version History: When a query like get_quarterly_report.sql is updated, the old version is often gone forever. If the new version introduces an error, rolling back is a manual, error-prone process. You can't see why a change was made or by whom.
• The Brittleness of Embedded Logic: A senior developer embeds a complex join directly into a microservice. Months later, a DBA changes a column name. The application breaks, and now you have to redeploy an entire service just to fix a single query.
• Lack of Reusability and Discovery: One team needs a list of users by email, and another needs it by ID. They both write similar-but-different queries from scratch, duplicating effort and creating potential for inconsistencies. There's no central registry to discover if the logic they need already exists.

This chaos isn't just a technical headache; it’s a business liability. It slows down development, introduces risk, and makes your data systems incredibly difficult to maintain and scale.

A Better Way: Queries as Intelligent API Agents

The solution is to stop thinking of data retrieval as a one-off script and start treating it as a first-class, version-controlled service. By applying the principles of Business-as-Code, we can transform a fragile query into a robust, intelligent Search Agent.

Imagine defining your data logic not as a string of SQL, but as a managed code object with clear characteristics:

• Version Controlled: It lives in your Git repository. Every change is a commit. Every commit can be reviewed, tested, and rolled back.
• Discoverable: It has a clear name (FindUserByEmail) and a description, making it easy for any developer in your organization to find and understand.
• Reusable: It exposes a simple, stable API contract. Anyone can call it without needing to know the underlying database schema, table names, or query language.
• Encapsulated: The complex retrieval logic is hidden inside a handler. You can refactor the entire data source from MongoDB to Postgres, and as long as the API contract remains the same, no consuming service will ever know the difference.

This is where Searches.do comes in. It’s a platform built from the ground up on this exact philosophy.

Searches.do: Your BaC Platform for Data Logic

Searches.do allows you to define complex data retrieval logic as simple, deployable, and intelligent APIs. You write your business logic as code, and we handle the rest.

Let's look at how simple it is to codify a common business need: finding a user by their email address.

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 version-controlled data retrieval logic lives here.
    // It can connect to any database, API, or data warehouse.
    const user = await db.collection('users').findOne({ email });
    return user;
  }
});

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

Let's break down why this is a massive leap forward:

1. Code, Not Scripts: The entire definition lives in a file like users.search.ts. You can commit it to Git, open a pull request for review, and track its history forever.
2. Self-Documenting API: The name, description, and parameters create a clear, documented API contract. New developers don't need to read SQL to understand what it does; they can just call the service.
3. Total Abstraction: The handler function encapsulates the messy details. Today it might be a simple findOne query. Tomorrow, it could be a multi-step process that checks a cache, queries a data warehouse, and calls a third-party API. The applications calling /findUserByEmail never have to change.
4. AI-Ready: You can easily integrate Large Language Models (LLMs) into your handler to perform semantic or natural language searches, transforming a simple lookup into a powerful conversational data agent.

Unlock Compounding Benefits

By adopting a Business-as-Code approach with Searches.do, you do more than just clean up your queries. You unlock compounding benefits that accelerate your entire development lifecycle.

• Faster Onboarding: Junior developers don't need to learn complex schemas. They can simply browse a library of available Search Agents and start using them immediately.
• Painless Refactoring: Need to migrate from a monolith database to microservices? Just update the handler logic in your Search Agents. Zero changes needed in your front-end or application code.
• Centralized Governance: All your business-critical data logic is in one place. It's easy to audit, secure, and manage.
• True Reusability: The FindUserByEmail agent can be used by the web app, the mobile app, the marketing analytics team, and an internal admin tool—all without rewriting a single line of logic.

Stop Writing Scripts. Start Building Assets.

Your data retrieval logic is a core business asset. It’s time to start treating it that way. Stop letting it decay in forgotten folders and un-versioned scripts.

By embracing the Business-as-Code philosophy, you can bring the discipline, reliability, and scalability of modern software development to your data layer.

Ready to bring version control and sanity to your data logic? Get started with Searches.do and transform your queries from disposable scripts into intelligent, reusable APIs.