4 futuristic databases to watch in 2025

In 2023, I delved into some groundbreaking databases that brought developers to the edge of their seats. While it’s true that the database world never sleeps, in 2025 we’ve curated four innovative databases that are reshaping the future of data management. Whether you’re a developer, data scientist, or simply a tech enthusiast, these are the ones to watch.

This article is adapted from the original blog post, Exploring 8 Futuristic Databases to Watch in 2023, published on the Semaphore Blog.

1. PlanetScale: The Git for Databases

Branch, test, merge, repeat—now for databases.

PlanetScale transforms a serverless MySQL platform with Git-style branching capabilities. Create separate branches for testing, experiment without fear, and merge changes seamlessly. Built on Vitess (the database technology powering YouTube), it’s engineered for scale and reliability.

-- Create a new branch for feature development
pscale branch create myapp add-user-preferences

-- Switch to the new branch
pscale connect myapp add-user-preferences

-- Make your changes
CREATE TABLE user_preferences (
    id BIGINT PRIMARY KEY AUTO_INCREMENT,
    user_id BIGINT NOT NULL,
    theme VARCHAR(50) DEFAULT 'light',
    notifications_enabled BOOLEAN DEFAULT true,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (user_id) REFERENCES users(id)
);

-- Create a deploy request (similar to a pull request)
pscale deploy-request create myapp add-user-preferences

First, we create a new branch called add-user-preferences using pscale branch create. This is similar to creating a Git branch, allowing us to make schema changes without affecting the production database. Then, we connect to our new branch using pscale connect, which establishes a secure connection to our development database. After that, the CREATE TABLE statement defines a new table for storing user preferences with:

An auto-incrementing primary key
A foreign key relationship to the users table
Default values for theme and notifications
Automatic timestamp tracking At the end, we create a deploy request, which is PlanetScale’s equivalent of a pull request, allowing team review before applying changes to production.

2. Dolt: The Git and Data Mashup

Imagine a database that lets you fork, clone, and merge just like Git. That’s Dolt! It’s the perfect solution for collaborative environments where version-controlled data workflows are a game-changer. Need to track who changed what and when? Dolt provides unparalleled visibility.

-- Clone a database
dolt clone dolthub/us-presidents

-- Create and switch to a new branch
dolt checkout -b update-2024

-- Make changes
INSERT INTO presidents 
  (id, name, party, terms)
VALUES 
  (47, 'Joe Biden', 'Democratic', '2021-present');

-- Commit your changes
dolt add presidents
dolt commit -m "Updated with current president"

-- View history
dolt log

We start with dolt clone that works exactly like Git clone, creating a local copy of a database repository. The checkout -b command next should feel familiar, it creates and switches to a new branch, isolating our changes. Then, we make data changes using standard SQL commands like INSERT. You can then stage your changes using the (dolt add) and commit them using the dolt commit, just like in Git. At any point, you can run the dolt log command that shows the complete history of changes, including who made them and when.

This is extremely intuitive for a developer who knows Git, isn’t it?

3. SurrealDB: Backend Development Made Easy

Reintroducing the Swiss Army knife of databases - SurrealDB.

SurrealDB brilliantly integrates graph, document, and relational models into one comprehensive package. It minimizes the need for separate APIs, making backend development smoother than ever.

-- Create a user and their posts with relationships
CREATE user:john SET 
    name = 'John Doe',
    email = 'john@example.com';

CREATE post:1 SET 
    title = 'My First Post',
    content = 'Hello, World!',
    author = user:john;

-- Graph-style query to fetch user with their posts
SELECT * FROM user:john -> wrote -> post;

-- Complex relationship query
LET $following = (SELECT -> following FROM user:john);
SELECT * FROM post 
WHERE author IN $following
ORDER BY created_at DESC
LIMIT 10;

The graph-style query (->) shows how easily we can traverse relationships, similar to graph databases. We can create variables using LET and store the result of some queries in that variable.

4. Pinecone: The Future of AI and ML Search

Pinecone is purpose-built for 2025, the AI era. As a vector database, it specializes in handling high-dimensional data and executing sophisticated similarity searches.

import pinecone

# Initialize Pinecone
pinecone.init(api_key="your-api-key")

# Create an index for product embeddings
pinecone.create_index("products", dimension=384)
index = pinecone.Index("products")

# Insert vector embeddings
index.upsert([
    ("prod1", [0.1, 0.2, ..., 0.9], {"name": "Blue T-Shirt"}),
    ("prod2", [0.2, 0.3, ..., 0.8], {"name": "Red Sweater"})
])

# Query similar products
results = index.query(
    vector=[0.15, 0.25, ..., 0.85],
    top_k=5,
    include_metadata=True
)

To begin with Pinecode, we first create an index specifying the vector dimension (384 in this case, common for many embedding models). The upsert operation shows how to store vectors with a unique ID (prod1, prod2). The query operation above demonstrates similarity search by providing a search vector where we request the top 5 most similar items, including metadata in the results.

Wrapping Up

From AI-ready tools to collaborative data workflows, these futuristic databases are solving today’s challenges while preparing for tomorrow’s demands. Whether you’re launching a startup or scaling an enterprise, there’s an innovative solution here for every data management need.

Take them for a spin and discover which one best fits your requirements in 2025. The future of data management has arrived—why not be at the forefront?