Unifying Your Data: How to Integrate PostgreSQL, MongoDB, and More with a Single API

In the modern world of application development, we live in a state of "polyglot persistence." You might use a robust PostgreSQL database for your core relational data, MongoDB for flexible user profiles and logging, and maybe even a specialized vector database to power new AI features. While this "right tool for the job" approach is powerful, it often creates a significant challenge for development teams: a fragmented and complex data access layer.

Developers are forced to constantly switch contexts between SQL, MongoDB Query Language (MQL), and other database-specific syntaxes. This leads to writing mountains of boilerplate code, maintaining multiple database clients, and building custom abstraction layers that are a headache to manage.

But what if you could communicate with all of them through a single, intelligent, and unified API? What if you could perform complex searches and CRUD operations on any database without writing a single line of native query language? This is no longer a dream; it's the reality a unified Data Access API provides.

The Problem: The High Cost of Data Fragmentation

When your application interacts with multiple database types, the hidden costs add up quickly:

• Increased Cognitive Load: A developer working on a single feature might have to write a SQL JOIN and a MongoDB aggregation pipeline in the same afternoon. This context switching slows down development and increases the likelihood of errors.
• Repetitive Boilerplate: For each new database, you need to add a new client, manage connection pools, and write data access logic for creating, reading, updating, and deleting records. It's repetitive, uncreative work.
• Inconsistent Internal APIs: The code to fetch a user from PostgreSQL looks completely different from the code that fetches a product document from MongoDB. This leads to an inconsistent and complicated codebase that's difficult for new developers to learn.
• Maintenance Overhead: Every custom abstraction layer you build becomes another piece of critical infrastructure you have to maintain, secure, and update.

The Solution: database.do - Your Data, Supercharged by AI

This is precisely the problem database.do was built to solve. It acts as an AI-native data gateway, providing a single point of access to all your disparate data sources.

database.do allows you to effortlessly access, search, and manage your databases using natural language or simple, consistent API calls. You can go from idea to a fully functional data layer in seconds.

Instead of wrestling with multiple clients and query languages, you interact with a clean, unified interface. Our platform translates your requests into optimized, native queries for any connected database—whether it's SQL, NoSQL, or a vector DB.

How It Works: From Simple API Call to Powerful Query

Imagine you want to find all active users named 'Jane Doe' from your users table, which lives in a PostgreSQL database. You'd typically write a SQL query. But with database.do, you write this:

import { Database } from '@do/sdk';

// Connect to your database agent
const db = new Database('YOUR_DATABASE_ID');

// Find all active users named 'Jane'
const activeJanes = await db.search({
  from: 'users',
  where: {
    name: 'Jane Doe',
    status: 'active'
  },
  limit: 10
});

console.log(activeJanes);

This simple, intuitive object is all you need. database.do intelligently translates this into the optimal SQL query for PostgreSQL. Now, what if your user data was in MongoDB? You would use the exact same code. No changes needed. database.do handles the translation to the correct MQL query behind the scenes.

This is the power of a true AI Database abstraction. You define what you want, and the system figures out how to get it.

More Than Just Search: A Unified CRUD API

A unified data layer isn't just for reading data. Managing data is just as important. database.do provides a simple and consistent CRUD API that works across all your databases.

• db.create({ into: 'products', data: { ... } })
• db.update({ in: 'users', where: { id: 123 }, data: { ... } })
• db.delete({ from: 'logs', where: { timestamp: ... } })

The methods are predictable and easy to remember. You no longer have to recall the specific syntax for INSERT, UPDATE, or DELETE in SQL versus their equivalents in MongoDB. This dramatically speeds up development and reduces bugs.

Key Questions Answered

We know handing over data access to a platform raises important questions. Here’s how we address the most common ones:

• What databases are supported? database.do supports a wide range of popular SQL and NoSQL databases, including PostgreSQL, MySQL, and MongoDB. We are also building first-class support for vector databases to supercharge your AI applications.
• Is it secure? Absolutely. Security is our top priority. All connections are encrypted, and access is managed through secure API keys and granular, role-based permissions to ensure your data is always protected.
• How does the AI-native search work? Our intelligent platform translates your simple, structured API calls into highly optimized database-specific queries. This unlocks the ability to perform complex searches and aggregations without needing to become a database expert.

Stop Juggling, Start Building

It's time to move past the complexity of a fragmented data landscape. By adopting a unified Data Access API like database.do, you can eliminate boilerplate, reduce cognitive load, and empower your team to build features faster.

Unify your PostgreSQL, MongoDB, and other data sources into a single, intelligent layer. Your developers will thank you.

Ready to simplify your data access? Visit database.do and go from idea to a fully functional data layer in seconds.