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You thought you knew databases?
What database to use in 2023?
Which database is the best?
Which database is mostly used?
Which database is perfect?
Which database is most powerful?
What database is better than SQL?
Is MongoDB still popular?
Why is MySQL so popular?
Best database?
Think again!
I'm about to introduce you to 25 funky fresh databases that'll blow your socks off.
From classic favorites to cutting-edge newcomers,
get ready for a wild ride through database land!
1. MySQL - The Classic
CREATE TABLE users (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255));
INSERT INTO users (name) VALUES ('John Rush');
SELECT * FROM users;
MySQL is the trusted granddaddy of databases – it's been around since the dawn of time and somehow keeps getting better.
With solid performance and compatibility with almost every programming language on Earth, this old-timer isn't going anywhere.
2. MongoDB - NoSQL Hipster Kid
// Connect to MongoDB and insert a document
const { MongoClient } = require("mongodb");
const uri = "mongodb://localhost:27017";
const client = new MongoClient(uri);
await client.connect();
const db = client.db("mydb");
await db.collection("users").insertOne({ name: "John Rush" });
MongoDB is that cool NoSQL kid who doesn't care about rules.
It's all about storing data in flexible JSON-like documents, making it the go-to choice for developers looking for a schema-less solution.
3. PostgreSQL - The Sophisticated One
CREATE TABLE users (id SERIAL PRIMARY KEY, name VARCHAR(255));
INSERT INTO users (name) VALUES ('John Rush');
SELECT * FROM users;
PostgreSQL might just be the perfect blend of performance, features, and elegance.
With its powerful query language and support for custom types, this open-source gem has earned its place among top databases.
4. Oracle - The Enterprise Behemoth
CREATE TABLE users (id NUMBER GENERATED BY DEFAULT ON NULL AS IDENTITY PRIMARY KEY, name VARCHAR2(255));
INSERT INTO users (name) VALUES ('John Rush');
SELECT * FROM users;
Oracle is like that popular kid at school who gets straight A's but also plays on every sports team – it does everything well!
Sure, you'll need deep pockets to keep up with this high-roller database system used by major enterprises worldwide.
5. Cassandra - Distributed Dynamo
from cassandra.cluster import Cluster
cluster = Cluster(["127.0.0.1"])
session = cluster.connect()
session.execute("""
CREATE KEYSPACE mykeyspace
WITH replication={'class':'SimpleStrategy', 'replication_factor':1}
""")
session.set_keyspace("mykeyspace")
session.execute("""
CREATE TABLE users (
id UUID PRIMARY KEY,
name text
)
""")
Cassandra is the queen bee of distributed databases – she's got scalability and fault tolerance for days! If you're looking to build a massive, high-performing application, this NoSQL database might be your new best friend.
6. Redis - The Speedster
import redis
r = redis.Redis(host="localhost", port=6379)
r.set("name", "John Rush")
print(r.get("name"))
Redis is that one friend who can sprint faster than Usain Bolt while juggling flaming chainsaws. This in-memory data store is blazingly fast and perfect for caching or real-time applications.
7. MariaDB - MySQL’s Open-Source Sibling
CREATE TABLE users (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255));
INSERT INTO users (name) VALUES ('John Rush');
SELECT * FROM users;
MariaDB shares much of its DNA with MySQL but focuses on being open-source and community-driven. It's like choosing between Pepsi and Coke – they both quench your thirst; it just depends on which flavor you prefer!
8. SQLite - The Lightweight Champion
import sqlite3
conn = sqlite3.connect(":memory:")
c = conn.cursor()
c.execute("""CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT);""")
c.execute("""INSERT INTO users (name) VALUES ('John Rush');""")
conn.commit()
for row in c.execute("SELECT * FROM users"):
print(row)
SQLite packs a punch in a featherweight package. It's a self-contained, serverless SQL database that's perfect for small projects and applications where simplicity is key.
9. YugabyteDB - The Postgres Powerhouse
CREATE TABLE users (id SERIAL PRIMARY KEY, name VARCHAR(255));
INSERT INTO users (name) VALUES ('John Rush');
SELECT * FROM users;
YugabyteDB takes the best of PostgreSQL and adds distributed magic sauce! This high-performance, cloud-native database supports horizontal scaling and multi-cloud deployments while maintaining full compatibility with Postgres.
10. Neo4j - All About Relationships
CREATE (john:User {name: "John Rush"});
MATCH (user:User) RETURN user;
Neo4j makes complicated relationships look like child's play with its graph-based data model. Traverse complex networks at lightning speeds – making it ideal for social networking or recommendation engines!
11. Dolt - Git Meets Database World
$ dolt init && dolt sql <<EOF
CREATE TABLE users (
id INT AUTO_INCREMENT PRIMARY KEY,
name VARCHAR(255)
);
INSERT INTO users(name) VALUES ("John Rush");
SELECT * FROM_users;
EOF
Dolt lets you branch out from the norm by combining MySQL-style databases with Git-like version control features! Experiment without fear; merge changes when ready in this innovative hybrid database system.
12. CockroachDB - NewSQL Innovator
CREATE TABLE users (id UUID DEFAULT gen_random_uuid() PRIMARY KEY, name STRING);
INSERT INTO users (name) VALUES ('John Rush');
SELECT * FROM users;
CockroachDB skitters into view as a powerful NewSQL solution built from the ground up for cloud-native environments. With Postgres compatibility and horizontal scaling, it's a force to be reckoned with!
13. Planetscale - Scaling MySQL to Infinity
CREATE TABLE users (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255));
INSERT INTO users (name) VALUES ('John Rush');
SELECT * FROM users;
Remember YouTube's struggles scaling MySQL? Well, Planetscale does just that! It takes VTest technology developed by YouTube and offers a fully managed serverless platform for scaling MySQL without breaking a sweat.
14. Google Cloud Spanner - The Global Database
const {Spanner} = require("@google-cloud/spanner");
const spanner = new Spanner();
const instance = spanner.instance("my-instance");
const database = instance.database("my-database");
await database.run({
sql: "CREATE TABLE Users (Id STRING(MAX) NOT NULL, Name STRING(MAX)) PRIMARY KEY(Id)"
});
await database.run({ sql: "INSERT Users (Id, Name) VALUES('1', 'John Rush')" });
Need consistent data across continents? Google Cloud Spanner has got you covered! This globally distributed SQL database ensures your data is always available while providing strong consistency guarantees.
15. Xata - Spreadsheet-like Simplicity
import xatasheet_client as xata
client = xata.Client()
sheet_id = client.create_sheet(title="Users")
rows_added_count = client.insert_rows(sheet_id= sheet_id,
rows=[{"column": "Name", "value": "John Rush"}])
print(f"Rows added count: {rows_added_count}")
Xata brings spreadsheet-level simplicity to relational databases! Built on top of Postgres and Elasticsearch, it offers full-text search, easy-to-visualize relationships, and an API for TypeScript and Python.
16. Amazon Aurora - AWS's Database Darling
import boto3
rds = boto3.client("rds-data")
response = rds.execute_statement(
resourceArn="arn:aws:rds:us-west-2:123456789012:cluster:mysql-cluster",
secretArn="arn:aws:secretsmanager:us-west-2::secret:mysql-pass",
sql="CREATE TABLE users (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(255));"
)
Amazon Aurora brings the power of MySQL or PostgreSQL to the AWS ecosystem with even better performance! It scales automatically while integrating seamlessly with other AWS services.
17. Microsoft SQL Server - The Trusted Workhorse
CREATE TABLE Users (Id INT IDENTITY(1,1) PRIMARY KEY NOT NULL,
Name NVARCHAR(MAX));
INSERT INTO Users(Name) VALUES(N'John Rush');
SELECT * FROM Users;
Microsoft SQL Server has been a reliable powerhouse in the database world for decades! With strong security features and integration with Azure cloud services, it remains a popular choice among businesses large and small.
18. Apache HBase - Big Data Behemoth
Configuration config = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(config);
Table table = connection.getTable(TableName.valueOf("users"));
Put putData = new Put(Bytes.toBytes("row"));
putData.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("name"), Bytes.toBytes("John Rush"));
table.put(putData);
ResultScanner resultsScanner = table.getScanner(new Scan());
for (Result res : resultsScanner) {
System.out.println(res);
}
Apache HBase is the go-to choice for those dealing with massive amounts of unstructured data! As a part of the Apache Hadoop ecosystem, it offers high write throughput and low-latency reads.
19. TimescaleDB - Time Series Titan
CREATE TABLE users (
time TIMESTAMPTZ NOT NULL,
name TEXT NOT NULL
);
SELECT create_hypertable('users', 'time');
INSERT INTO users(time, name) VALUES(NOW(), 'John Rush');
SELECT * FROM users;
TimescaleDB takes PostgreSQL and adds a time series twist! Built as an extension to PostgreSQL, this newcomer shines when you're dealing with large-scale data that changes over time – such as IoT devices or analytics.
20. FaunaDB - Serverless Superstar
const faunadb = require("faunadb");
const q = faunadb.query;
const client = new faunadb.Client({ secret: "your-secret-key" });
await client.query(
q.CreateCollection({ name: "users" })
);
await client.query(
q.Create(q.Collection("users"), { data: { name: "John Rush" } })
);
FaunaDB swoops in as a serverless cloud database designed for modern applications. It offers GraphQL and custom functions, along with global consistency and built-in security features.
21. InfluxData - DevOps Dynamo
# Write Data
curl --request POST 'http://localhost:8086/write?db=mydb' \
--data-binary 'user,name="John Rush"'
# Query Data
curl --get 'http://localhost:8086/query' \
--data-urlencode "db=mydb&q=SELECT * FROM user"
InfluxData is tailor-made for monitoring metrics and events in your infrastructure. Its powerful query language can help identify trends, anomalies, or other valuable insights from your time-series data.
22. Memgraph - Graph Database Prodigy
from mgclient import connect
connection = connect(host='127.0.0.1', port=7687)
cursor = connection.cursor()
cursor.execute('CREATE (:User{name:"John Rush"})')
cursor.execute('MATCH (u:User) RETURN u.name')
for row in cursor.fetchall():
print(row)
Memgraph is a high-performance, in-memory graph database built for real-time analytics. Its compatibility with Cypher query language makes it an excellent choice for those familiar with Neo4j.
23. Riak KV - Key-Value Connoisseur
import riak
client = riak.RiakClient()
bucket = client.bucket("users")
user_key = bucket.new("johnrush", data={"name": "John Rush"})
user_key.store()
fetched_user = bucket.get("johnrush")
print(fetched_user.data)
Riak KV provides a highly available and distributed key-value store that can handle heavy loads without breaking a sweat. It's perfect for use cases like gaming, ad tech, or mobile applications.
24. KeyDB - Redis on Steroids
from keydb import Client as KeyDBClient
keydb_client = KeyDBClient(host="localhost", port=6379)
keydb_client.set("name", "John Rush")
print(keydb_client.get("name"))
KeyDB takes everything you love about Redis and cranks it up to eleven! With multithreading support and improved performance, this open-source alternative might just be your new go-to caching solution.
25. MeiliSearch - Full-Text Search Extraordinaire
import meilisearch
client = meilisearch.Client('http://127.0.0.1:7700')
index = client.create_index(uid='users')
index.add_documents([{'id': '1', 'name': 'John Rush'}])
results = index.search('John')
print(results.hits)
MeiliSearch brings the power of full-text search to your fingertips with minimal setup! This ultra-fast, typo-tolerant search engine can be integrated into any application in a matter of minutes.
There you have it – 25 databases that'll make you rethink everything you thought you knew about data storage!
If this tour has left you hungry for more amazing content, don't forget to follow me on Twitter @johnrushx where I share my thoughts on transforming the software development world.
Hi, I'm John, Multi Startup Builder.
I enjoy both coding and marketing.
See all my 20 products here
→ johnrush.me
→ my BIO
→ Say Hi to me On Twitter
→ Try my website builder
Top comments (33)
Dgraph, Supabase, Firebase, TigerGraph, RedisGraph, SurrealDb, EdgeDb, Hasura, Memgraph, 8base, Grafbase, Prisma, Drizzle, TypeDb, Neptune, Couchbase, Singlestore, Appwrite -- although there is a difference between a db, a db platform, and a db orm
This are not db's and yes I know you wrote this already but... why the hell even write it here.
true, I just tried to simplify it for an average reader
I think we should add YDB to the list of distributed SQL databases.
ydb.tech/
There's also a column-oriented DBMS Clickhouse initially developed by the same company. It was designed with analytics in mind, I believe.
clickhouse.com/
edit: typo
yeah, good one, gonna include in my next article
never heard of it, gonna give it a try
YDB most good for read loads, benchmark: blog.ydb.tech/ycsb-performance-ser...
hoping to see gundb make the next article.
Essentially like firebase but open source and awesomer - it’s an offline-first, distributed P2P, real-time graph db built on top of browser storage 🤯
this is truly inspiring tech, check it out!
Superb article, some crackers I'm going to have a look and the starter code is a great way to get people playing with some of these!
I will say that with 25 years working with Oracle, make sure you pay the licenses or keep the heck away. If you think the IRS/HMRC are a nightmare for audits, you do not want to be working in a big Oracle RBDMS set up when Oracle decide to do a deep audit on your kit!
Oracel and MsSql are just to credit their corporate market share :)
Why not adding
duckdb?DuckDB Snippets
Share and vote for your favorites with the DuckDB community.
gonna give it a try first
This article is informative and insightful.
What happen with SQL Server? Why nobody use this database nowadays?
it was super expensive back in those days, Im not sure if it's free now, but running MS Sql server was not cheap
In my university we use this technology but in the world of the living the people use non-relational database.
PG for me, simple easy to read and write. I'm a noob though
Agree, PG and Mongo are best for quick start
Shoutout to:
Where is my beloved DynamoDB 🤣
DynamoDB, how could I forget, I actually use it a lot.
Gonna add it to some of my next articles
Funny way of introducing different DB options for new or existing projects! Tha nks!
I wanted to ask about RavenDb, what do you think? It is not listed here
I will try it out and add to next articles
Ok, next time I would use KeyDB in my project I will imagine how I lift two girls with no complexity :D
Thanks for this summary John
I keep rewatching the articles just for the memes
Many options and we still seeing startups finding a niche to create new database targeted for specific problem or use case.
as long as we, as developers, get excited about new toys, this will be the world.
Which is good I think. Competition drives the innovation. The dev world is seeing new names every day and a week. New frameworks, languages, dbs and etc
Thank you @johnrushx for including Memgraph in your list :)
Do you perhaps have Memgraph based project that you can share with us?
don't have it in prod, just played around with it in my sandbox
If you are interested in database, start with this one SQL, NoSql, and 5 DB types you'll never need