Hi, I'm John, Multi Startup Builder.
I enjoy both coding and marketing.
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Hey there, fellow codebreakers!
Grab your favorite snack, put on your wizard hat, and let me guide you through this journey with a touch of humor, sarcasm, memes – and yes, even some solid information!
1. Relational
"The relational database management system is one of the most successful ideas in history." – Me, just now
SELECT * FROM users WHERE age > 18;
Relational databases have dominated the scene for decades with SQL ruling them all. They use tables to store data, making it easy to establish relationships between different entities. Oracle dominates enterprise while MySQL backs platforms like YouTube and Facebook.
But hey! It's not perfect... let's see what else is out there!
2. Key-Value
db.set("key", "value");
Next up, key-value stores! Think Redis or Amazon DynamoDB here. These bad boys provide blazing-fast access to values based on their keys - like looking up someone in your phonebook using their name (remember those?). Great for caching and super-scalable!
3.Document
{
"_id": "123",
"name": "Bob",
"job": {
"title": "CEO",
"company": {
"_id": '456',
'name': 'MarsX'
}
}
Document-based databases like MongoDB store semi-structured JSON-like data. They're super flexible, so you can add or remove fields on the fly without losing sleep over schema changes (unlike in relational databases). Perfect for apps with ever-evolving requirements!
4.Wide Column
from cassandra.cluster import Cluster
cluster = Cluster()
session = cluster.connect('mykeyspace')
result = session.execute("SELECT * FROM users")
Wide column stores like Apache Cassandra and HBase are designed to store massive amounts of data across multiple nodes. Think billions of rows and millions of columns! Great for time series data, IoT, and analytics - Big Data nerds rejoice!
5.Graph
MATCH (u:User)-[:FRIENDS]->(f) WHERE u.name="Alice" RETURN f;
Relationship status: It's complicated. Graph databases like Neo4j shine when it comes to handling complex relationships between entities. A perfect match for social networks, recommendation engines, and anything that involves traversing through a web of interconnectedness.
6.Search Engine
client.search({
index: 'books',
body: {
query: {
match_phrase_prefix: {
title: "The Hitchhiker's"
}
}
}
});
Search engine databases such as Elasticsearch go beyond your average database by offering advanced text search capabilities. Find what you need even if you misspell "Hitchhiker's Guide to the Galaxy." Say goodbye to Ctrl-F forever!
7.Multi-model
Last but not least... multi-model databases! Why choose one paradigm when you can have them all? Databases like ArangoDB let you work with key-value pairs, documents, AND graphs. It's the Swiss Army Knife of databases - and who doesn't want that in their toolbox?
And there you have it!
Now go forth and conquer your data challenges with newfound wisdom, wit, sarcasm, and maybe even a few laughs. Happy coding!
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 (3)
Nice and concise and sarcastic? Definately Informative!!
I dumped SQL for NoSQL plain JSON Document db only for small dashboard projects. Here is an example of a Plain Javascript Stacked Bar Chart Sales Dashboard with a pure JSON Backend (data stored in AWS S3) and also no JS Libraries
dev.to/rickdelpo1/how-to-populate-...
I wrote a Dev article on deciding whether to SQL or to NoSQL
you can find it here
dev.to/rickdelpo1/to-sql-or-to-nos...
Personally I've used NoSql in most project, since it's schemaless and much better for quick iterations.