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How to Choose the Right Database for Your Data Project

· 4 min read
Tom Fynes
Tom Fynes
Data Engineer @ OptumUK

Choosing a database feels a bit like choosing a car - there's no single "best" option, only the best option for your specific needs. Let's navigate this together!

The Big Question: SQL or NoSQL?​

This is usually where everyone starts, but here's the thing - it's not an either/or decision anymore. Many projects use both!

SQL Databases (Relational)​

When to use SQL databases:

  • You need ACID compliance (Atomicity, Consistency, Isolation, Durability)
  • Your data has clear relationships
  • You need complex queries and joins
  • Data integrity is critical

Popular choices:

  • PostgreSQL: Feature-rich, excellent for most use cases
  • MySQL: Widely supported, great for web applications
  • SQL Server: Enterprise-grade, Windows ecosystem

Here's a simple example of structured data that fits perfectly in SQL:

-- Creating related tables
CREATE TABLE customers (
    customer_id SERIAL PRIMARY KEY,
    name VARCHAR(100),
    email VARCHAR(100) UNIQUE,
    created_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE orders (
    order_id SERIAL PRIMARY KEY,
    customer_id INTEGER REFERENCES customers(customer_id),
    total_amount DECIMAL(10,2),
    order_date TIMESTAMP DEFAULT NOW()
);

-- Easy to query relationships
SELECT 
    c.name,
    COUNT(o.order_id) as total_orders,
    SUM(o.total_amount) as total_spent
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.name;

NoSQL Databases​

When to use NoSQL:

  • You need horizontal scalability
  • Your schema changes frequently
  • You're working with unstructured data
  • You need high write throughput

Document Databases (MongoDB, Couchbase)​

Great for storing JSON-like documents:

# MongoDB example with pymongo
from pymongo import MongoClient

client = MongoClient('mongodb://localhost:27017/')
db = client['ecommerce']

# Flexible schema - each document can be different
product = {
    "name": "Laptop",
    "price": 999.99,
    "specs": {
        "ram": "16GB",
        "storage": "512GB SSD"
    },
    "tags": ["electronics", "computers"],
    "in_stock": True
}

db.products.insert_one(product)

# Easy nested queries
laptops = db.products.find({"tags": "computers", "price": {"$lt": 1500}})

Key-Value Stores (Redis, DynamoDB)​

Perfect for caching and session management:

import redis

r = redis.Redis(host='localhost', port=6379, db=0)

# Super fast read/write
r.set('user:1001:session', 'abc123xyz', ex=3600) # Expires in 1 hour
session = r.get('user:1001:session')

# Great for counters
r.incr('page:views')

Column-Family Stores (Cassandra, HBase)​

Ideal for time-series and analytics:

# Cassandra example
from cassandra.cluster import Cluster

cluster = Cluster(['127.0.0.1'])
session = cluster.connect('sensor_data')

# Optimized for write-heavy workloads
query = """
    INSERT INTO temperature_readings (sensor_id, timestamp, temperature)
    VALUES (%s, %s, %s)
"""
session.execute(query, ('sensor_001', datetime.now(), 22.5))

Data Warehouses (The Analytics Powerhouses)​

For analytics and reporting, consider specialized warehouses:

  • Snowflake: Cloud-native, auto-scaling
  • BigQuery: Google's serverless warehouse
  • Redshift: AWS's data warehouse solution
-- BigQuery example - handling billions of rows efficiently
SELECT 
    DATE(timestamp) as date,
    user_id,
    COUNT(*) as events,
    SUM(revenue) as daily_revenue
FROM `project.dataset.events`
WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
GROUP BY date, user_id
HAVING daily_revenue > 100
ORDER BY daily_revenue DESC;

Decision Framework​

Here's a simple flowchart to help you decide:

Start here: What's your primary use case?

→ Transactional processing (banking, e-commerce orders)
Use: PostgreSQL, MySQL

→ Real-time analytics (dashboards, metrics)
Use: ClickHouse, TimescaleDB

→ Content management (blogs, catalogs)
Use: MongoDB, PostgreSQL with JSONB

→ Caching/Session storage
Use: Redis, Memcached

→ Large-scale analytics (data science, BI)
Use: Snowflake, BigQuery, Redshift

→ IoT/Time-series data
Use: InfluxDB, TimescaleDB, Cassandra

Practical Tips​

Consider these factors:

  • Team expertise: Use what your team knows well
  • Cost: Cloud databases can get expensive at scale
  • Maintenance: Managed services reduce operational burden
  • Vendor lock-in: Can you migrate if needed?

Don't over-engineer: PostgreSQL can handle most use cases beautifully. Start simple and evolve.

A Hybrid Approach Example​

Many successful systems use multiple databases:

# Example: E-commerce architecture
class OrderService:
    def __init__(self):
        self.postgres = PostgresConnection() # Transactional data
        self.redis = RedisConnection() # Cache & sessions
        self.mongo = MongoConnection() # Product catalog
    
    def create_order(self, order_data):
        # Write to PostgreSQL for consistency
        order_id = self.postgres.insert_order(order_data)
        
        # Cache user's recent order
        self.redis.set(f"user:{order_data['user_id']}:last_order", 
                      order_id, ex=86400)
        
        # Update product inventory in MongoDB
        self.mongo.update_inventory(order_data['items'])
        
        return order_id

Resources to Learn More​

Final Thoughts​

The "right" database depends on your specific needs. When in doubt, PostgreSQL is a fantastic default choice that can scale surprisingly far. You can always add specialized databases as your needs grow.