NoSQL Databases Explained: Types, Use Cases & Core Characteristics

Technical writer

Infrastructure

04.06.2025

16 min read

NoSQL (which stands for "Not Only SQL") represents a new class of data management systems that deviate from the traditional relational approach to information storage. Unlike conventional DBMSs, such as MySQL or PostgreSQL, which store data in tables with fixed structures and strict relationships, NoSQL offers more flexible methods for organizing and storing information. This technology doesn't reject SQL; rather, it expands the ways to handle data.

The origin of the term NoSQL has an interesting backstory that began not with technology but with the name of a tech conference. In 2009, organizers of a database event in San Francisco adopted the term, and it unexpectedly caught on in the industry. Interestingly, a decade earlier, in 1998, developer Carlo Strozzi had already used the term "NoSQL" for his own project, which had no connection to modern non-relational systems.

Modern NoSQL databases fall into several key categories of data storage systems. These include:

Document-oriented databases (led by MongoDB)
Key-value stores (e.g., Redis)
Graph databases (Neo4j is a prominent example)
Column-family stores (such as ClickHouse)

The unifying feature among these systems is their rejection of the classic SQL language in favor of proprietary data processing methods.

Unlike relational DBMSs, where SQL serves as a standardized language for querying and joining data through operations like JOIN and UNION, NoSQL databases have developed their own query languages. Each NoSQL database offers a unique syntax for manipulating data. Here are some examples:

// MongoDB (uses a JavaScript-like syntax):
db.users.find({ age: { $gt: 21 } })

// Redis (uses command-based syntax):
HGET user:1000 email
SET session:token "abc123"

NoSQL databases are particularly efficient in handling large volumes of unstructured data. A prime example is the architecture of modern social media platforms, where MongoDB enables storage of a user's profile, posts, responses, and activity in a single document, thereby optimizing data retrieval performance.

NoSQL vs SQL: Relational and Non-Relational Databases

The evolution of NoSQL databases has paralleled the growing complexity of technological and business needs. The modern digital world, which generates terabytes of data every second, necessitated new data processing approaches. As a result, two fundamentally different data management philosophies have emerged:

Relational approach, focused on data integrity and reliability
NoSQL approach, prioritizing adaptability and scalability

Each concept is grounded in its own core principles, which define its practical applications.

Relational systems adhere to ACID principles:

Atomicity ensures that transactions are all-or-nothing.
Consistency guarantees that data remains valid throughout.
Isolation keeps concurrent transactions from interfering.
Durability ensures that once a transaction is committed, it remains so.

NoSQL systems follow the BASE principles:

Basically Available – the system prioritizes continuous availability.
Soft state – the system state may change over time.
Eventually consistent – consistency is achieved eventually, not instantly.

Key Differences:

Aspect	Relational Databases	NoSQL Databases
Data Organization	Structured in predefined tables and schemas	Flexible format, supports semi-structured/unstructured data
Scalability	Vertical (via stronger servers)	Horizontal (adding more nodes to the cluster)
Data Integrity	Maintained at the DBMS core level	Managed at the application level
Performance	Efficient for complex transactions	High performance in basic I/O operations
Data Storage	Distributed across multiple interrelated tables	Groups related data into unified blocks/documents

These fundamental differences define their optimal use cases:

Relational systems are irreplaceable where data precision is critical (e.g., financial systems).
NoSQL solutions excel in processing high-volume data flows (e.g., social media, analytics platforms).

Key Features and Advantages of NoSQL

Most NoSQL systems are open source, allowing developers to explore and modify the core system without relying on expensive proprietary software.

Schema Flexibility

One of the main advantages of NoSQL is its schema-free approach. Unlike relational databases, where altering the schema often requires modifying existing records, NoSQL allows the dynamic addition of attributes without reorganizing the entire database.

// MongoDB: Flexible schema supports different structures in the same collection
db.users.insertMany([
  { name: "Emily", email: "emily@email.com" },
  { name: "Maria", email: "maria@email.com", phone: "+35798765432" },
  { name: "Peter", social: { twitter: "@peter", facebook: "peter.fb" } }
])

Horizontal Scalability

NoSQL databases employ a fundamentally different strategy for boosting performance. While traditional relational databases rely on upgrading a single server, NoSQL architectures use distributed clusters. Performance is improved by adding nodes, with workload automatically balanced across the system.

Sharding and Replication

NoSQL databases support sharding—a method of distributing data across multiple servers. Conceptually similar to RAID 0 (striping), sharding enables:

Enhanced system performance
Improved fault tolerance
Efficient load distribution

High Performance

NoSQL systems offer exceptional performance due to optimized storage mechanisms and avoidance of resource-heavy operations like joins. They perform best in scenarios such as:

Basic read/write operations
Large-scale data management
Concurrent user request handling
Unstructured data processing

Handling Unstructured Data

NoSQL excels in working with:

Large volumes of unstructured data
Heterogeneous data types
Rapidly evolving data structures

Support for Modern Technologies

NoSQL databases integrate well with:

Cloud platforms
Microservice architectures
Big Data processing systems
Modern development frameworks

Cost Efficiency

NoSQL solutions can be cost-effective due to:

Open-source licensing
Efficient use of commodity hardware
Scalability using standard servers
Reduced administrative overhead

Main Types of NoSQL Databases

In modern distributed system development, several core types of NoSQL solutions are distinguished, each with a mature ecosystem and strong community support.

Document-Oriented Databases

Document-based systems are the most mature and widely adopted type of NoSQL databases. MongoDB, the leading technology in this segment, is the benchmark example of document-oriented data storage architecture.

Data Storage Principle

In document-oriented databases, information is stored as documents grouped into collections. Unlike relational databases, where data is distributed across multiple tables, here, all related information about an object is contained within a single document.

Example of a user document with orders:

{
  "_id": ObjectId("507f1f77bcf86cd799439011"),
  "user": {
    "username": "stephanie",
    "email": "steph@example.com",
    "registered": "2024-02-01"
  },
  "orders": [
    {
      "orderId": "ORD-001",
      "date": "2024-02-02",
      "items": [
        {
          "name": "Phone",
          "price": 799.99,
          "quantity": 1
        }
      ],
      "status": "delivered"
    }
  ],
  "preferences": {
    "notifications": true,
    "language": "en"
  }
}

Basic Operations with MongoDB

// Insert a document
db.users.insertOne({
  username: "stephanie",
  email: "steph@example.com"
})

// Find documents
db.users.find({ "preferences.language": "en" })

// Update data
db.users.updateOne(
  { username: "stephanie" },
  { $set: { "preferences.notifications": false }}
)

// Delete a document
db.users.deleteOne({ username: "stephanie" })

Advantages of the Document-Oriented Approach

Flexible Data Schema

Each document can have its own structure
Easy to add new fields
No need to modify the overall database schema

Natural Data Representation

Documents resemble programming objects
Intuitive structure
Developer-friendly

Performance

Fast retrieval of complete object data
Efficient handling of nested structures
Horizontal scalability

Working with Hierarchical Data

Naturally stores tree-like structures
Convenient nested object representation
Effective processing of complex structures

Use Cases

The architecture is particularly effective in:

Developing systems with dynamically evolving data structures
Processing large volumes of unstandardized data
Building high-load distributed platforms

Typical Use Scenarios

Digital content management platforms
Distributed social media platforms
Enterprise content organization systems
Event aggregation and analytics services
Complex analytical platforms

Key-Value Stores

Among key-value stores, Redis (short for Remote Dictionary Server) holds a leading position in the NoSQL market. A core architectural feature of this technology is that the entire data set is stored in memory, ensuring exceptional performance.

Working Principle

The architecture of key-value stores is based on three fundamental components for each data record:

Unique key (record identifier)
Associated data (value)
Optional TTL (Time To Live) parameter

Data Types in Redis

# Strings
SET user:name "Stephanie"
GET user:name

# Lists
LPUSH notifications "New message"
RPUSH notifications "Payment received"

# Sets
SADD user:roles "admin" "editor"
SMEMBERS user:roles

# Hashes
HSET user:1000 name "Steph" email "steph@example.com"
HGET user:1000 email

# Sorted Sets
ZADD leaderboard 100 "player1" 85 "player2"
ZRANGE leaderboard 0 -1

Key Advantages

High Performance

In-memory operations
Simple data structure
Minimal overhead

Storage Flexibility

Support for multiple data types
Ability to set data expiration
Atomic operations

Reliability

Data persistence options
Master-slave replication
Clustering support

Typical Use Scenarios

Caching

# Cache query results
SET "query:users:active" "{json_result}"
EXPIRE "query:users:active" 3600  # Expires in one hour

Counters and Rankings

# Increase view counter
INCR "views:article:1234"

# Update ranking
ZADD "top_articles" 156 "article:1234"

Message Queues

# Add task to queue
LPUSH "task_queue" "process_order:1234"

# Get task from queue
RPOP "task_queue"

Redis achieves peak efficiency when deployed in systems with intensive operational throughput, where rapid data access and instant processing are critical. A common architectural solution is to integrate Redis as a high-performance caching layer alongside the primary data store, significantly boosting the overall application performance.

Graph Databases

Graph DBMS (Graph Databases) stand out among NoSQL solutions due to their specialization in managing relationships between data entities. In this segment, Neo4j has established a leading position thanks to its efficiency in handling complex network data structures where relationships between objects are of fundamental importance.

Core Components

Nodes

Represent entities
Contain properties
Have labels

Relationships

Connect nodes
Are directional
Can contain properties
Define the type of connection

Example of a Graph Model in Neo4j

// Create nodes
CREATE (anna:Person { name: 'Anna', age: 30 })
CREATE (mary:Person { name: 'Mary', age: 28 })
CREATE (post:Post { title: 'Graph Databases', date: '2024-02-04' })

// Create relationships
CREATE (anna)-[:FRIENDS_WITH]->(mary)
CREATE (anna)-[:AUTHORED]->(post)
CREATE (mary)-[:LIKED]->(post)

Typical Queries

// Find friends of friends
MATCH (person:Person {name: 'Anna'})-[:FRIENDS_WITH]->(friend)-[:FRIENDS_WITH]->(friendOfFriend)
RETURN friendOfFriend.name

// Find most popular posts
MATCH (post:Post)<-[:LIKED]-(person:Person)
RETURN post.title, count(person) as likes
ORDER BY likes DESC
LIMIT 5

Key Advantages

Natural Representation of Relationships

Intuitive data model
Efficient relationship storage
Easy to understand and work with

Graph Traversal Performance

Fast retrieval of connected data
Efficient handling of complex queries
Optimized for recursive queries

Practical Applications

Social Networks

// Friend recommendations
MATCH (user:Person)-[:FRIENDS_WITH]->(friend)-[:FRIENDS_WITH]->(potentialFriend)
WHERE user.name = 'Anna' AND NOT (user)-[:FRIENDS_WITH]->(potentialFriend)
RETURN potentialFriend.name

Recommendation Systems

// Recommendations based on interests
MATCH (user:Person)-[:LIKES]->(product:Product)<-[:LIKES]-(otherUser)-[:LIKES]->(recommendation:Product)
WHERE user.name = 'Anna' AND NOT (user)-[:LIKES]->(recommendation)
RETURN recommendation.name, count(otherUser) as frequency

Routing

// Find shortest path
MATCH path = shortestPath(
  (start:Location {name: 'A'})-[:CONNECTS_TO*]->(end:Location {name: 'B'})
)
RETURN path

Usage Highlights

Essential when working with complex, interrelated data structures
Maximum performance in processing cyclic and nested queries
Enables flexible design and management of multi-level relationships

Neo4j and similar platforms for graph database management show exceptional efficiency in systems where relationship processing and deep link analysis are critical. These tools offer advanced capabilities for managing complex network architectures and detecting patterns in structured sets of connected data.

Columnar Databases

The architecture of these systems is based on column-oriented storage of data, as opposed to the traditional row-based approach. This enables significant performance gains for specialized queries. Leading solutions in this area include ClickHouse and HBase, both recognized as reliable enterprise-grade technologies.

How It Works

Traditional (row-based) storage:

Row1: [id1, name1, email1, age1]  
Row2: [id2, name2, email2, age2]

Column-based storage:

Column1: [id1, id2]  
Column2: [name1, name2]  
Column3: [email1, email2]  
Column4: [age1, age2]

Key Characteristics

Storage Structure

Data is grouped by columns
Efficient compression of homogeneous data
Fast reading of specific fields

Scalability

Horizontal scalability
Distributed storage
High availability

Example Usage with ClickHouse

-- Create table
CREATE TABLE users (
    user_id UUID,
    name String,
    email String,
    registration_date DateTime
) ENGINE = MergeTree()
ORDER BY (registration_date, user_id);

-- Insert data
INSERT INTO users (user_id, name, email, registration_date)
VALUES (generateUUIDv4(), 'Anna Smith', 'anna@example.com', now());

-- Analytical query
SELECT 
    toDate(registration_date) as date,
    count(*) as users_count
FROM users 
GROUP BY date
ORDER BY date;

Key Advantages

Analytical Efficiency

Fast reading of selected columns
Optimized aggregation queries
Effective with large datasets

Data Compression

Superior compression of uniform data
Reduced disk space usage
I/O optimization

Typical Use Cases

Big Data

-- Log analysis with efficient aggregation
SELECT 
    event_type,
    count() as events_count,
    uniqExact(user_id) as unique_users
FROM system_logs 
WHERE toDate(timestamp) >= '2024-01-01'
GROUP BY event_type
ORDER BY events_count DESC;

Time Series

-- Aggregating metrics by time intervals
SELECT 
    toStartOfInterval(timestamp, INTERVAL 5 MINUTE) as time_bucket,
    avg(cpu_usage) as avg_cpu,
    max(cpu_usage) as max_cpu,
    quantile(0.95)(cpu_usage) as cpu_95th
FROM server_metrics
WHERE server_id = 'srv-001'
    AND timestamp >= now() - INTERVAL 1 DAY
GROUP BY time_bucket
ORDER BY time_bucket;

Analytics Systems

-- Advanced user statistics
SELECT 
    country,
    count() as users_count,
    round(avg(age), 1) as avg_age,
    uniqExact(city) as unique_cities,
    sumIf(purchase_amount, purchase_amount > 0) as total_revenue,
    round(avg(purchase_amount), 2) as avg_purchase
FROM user_statistics
GROUP BY country
HAVING users_count >= 100
ORDER BY total_revenue DESC
LIMIT 10;

Usage Highlights

Maximum performance in systems with read-heavy workloads
Proven scalability for large-scale data processing
Excellent integration in distributed computing environments

Columnar database management systems show exceptional efficiency in projects requiring deep analytical processing of large datasets. This is particularly evident in areas such as enterprise analytics, real-time performance monitoring systems, and platforms for processing timestamped streaming data.

Full-Text Databases (OpenSearch)

The OpenSearch platform, built on the architectural principles of Elasticsearch, is a comprehensive ecosystem for high-performance full-text search and multidimensional data analysis. This solution, designed according to distributed systems principles, stands out for its capabilities in data processing, intelligent search, and the creation of interactive visualizations for large-scale datasets.

Key Features

Full-Text Search

// Search with multilingual support
GET /products/_search
{
  "query": {
    "multi_match": {
      "query": "wireless headphones",
      "fields": ["title", "description"],
      "type": "most_fields"
    }
  }
}

Data Analytics

// Aggregation by categories
GET /products/_search
{
  "size": 0,
  "aggs": {
    "popular_categories": {
      "terms": {
        "field": "category",
        "size": 10
      }
    }
  }
}

Key Advantages

Efficient Search

Fuzzy search support
Result ranking
Match highlighting
Autocomplete functionality

Analytical Capabilities

Complex aggregations
Statistical analysis
Data visualization
Real-time monitoring

Common Use Cases

E-commerce Search

Product search
Faceted navigation
Product recommendations
User behavior analysis

Monitoring and Logging

Metrics collection
Performance analysis
Anomaly detection
Error tracking

Analytical Dashboards

Data visualization
Business metrics
Reporting
Real-time analytics

OpenSearch is particularly effective in projects that require advanced search and data analytics. At Hostman, OpenSearch is available as a managed service, simplifying integration and maintenance.

When to Choose NoSQL?

The architecture of various database management systems has been developed with specific use cases in mind, so choosing the right tech stack should be based on a detailed analysis of your application's requirements.In modern software development, a hybrid approach is becoming increasingly common, where multiple types of data storage are integrated into a single project to achieve maximum efficiency and extended functionality.

NoSQL systems do not provide a one-size-fits-all solution. When designing your data storage architecture, consider the specific nature of the project and its long-term development strategy.

Choose NoSQL databases when the following matter:

Large-scale Data Streams

Efficient handling of petabyte-scale storage
High-throughput read and write operations
Need for horizontal scalability

Dynamic Data Structures

Evolving data requirements
Flexibility under uncertainty

Performance Prioritization

High-load systems
Real-time applications
Services requiring high availability

Unconventional Data Formats

Networked relationship structures
Time-stamped sequences
Spatial positioning

Stick with Relational Databases when you need:

Guaranteed Integrity

Banking transactions
Electronic health records
Mission-critical systems

Complex Relationships

Multi-level data joins
Complex transactional operations
Strict ACID compliance

Immutable Structure

Fixed requirement specifications
Standardized business processes
Formalized reporting systems

Practical Recommendations

Hybrid Approach

// Using Redis for caching
// alongside PostgreSQL for primary data
const cached = await redis.get(`user:${id}`);
if (!cached) {
    const user = await pg.query('SELECT * FROM users WHERE id = $1', [id]);
    await redis.set(`user:${id}`, JSON.stringify(user));
    return user;
}
return JSON.parse(cached);

Gradual Transition

Start with a pilot project
Test performance
Evaluate support costs

Decision-Making Factors

Technical Aspects

Data volume
Query types
Scalability requirements
Consistency model

Business Requirements

Project budget
Development timeline
Reliability expectations
Growth plans

Development Team

Technology expertise
Availability of specialists
Maintenance complexity

Infrastructure

04.06.2025

16 min read

Similar

Infrastructure

NoSQL Databases Explained: Types, Use Cases & Core Characteristics

NoSQL vs SQL: Relational and Non-Relational Databases

Relational systems adhere to ACID principles:

NoSQL systems follow the BASE principles:

Key Features and Advantages of NoSQL

Schema Flexibility

Horizontal Scalability

Sharding and Replication

High Performance

Handling Unstructured Data

Support for Modern Technologies

Cost Efficiency

Main Types of NoSQL Databases

Document-Oriented Databases

Data Storage Principle

Basic Operations with MongoDB

Advantages of the Document-Oriented Approach

Use Cases

Key-Value Stores

Working Principle

Data Types in Redis

Key Advantages

Typical Use Scenarios

Graph Databases

Core Components

Example of a Graph Model in Neo4j

Typical Queries

Key Advantages

Practical Applications

Usage Highlights

Columnar Databases

How It Works

Key Characteristics

Example Usage with ClickHouse

Key Advantages

Typical Use Cases

Usage Highlights

Full-Text Databases (OpenSearch)

Key Features

Key Advantages

Common Use Cases

When to Choose NoSQL?

Choose NoSQL databases when the following matter:

Stick with Relational Databases when you need:

Practical Recommendations

Hybrid Approach

Decision-Making Factors

Similar

What Is Swagger and How It Makes Working with APIs Easier

AI Music Generation: Complete Guide and Comparison

Best ChatGPT Prompts for Better Answers

Do you have questions, comments, or concerns?

Do you have questions,
comments, or concerns?