Introduction to NoSQL

The NoSQL system or "Not Only SQL" is essentially a database that is made specifically for unstructured and semi-structured data in very large quantities. Unlike Conventional Relational Databases, where data are organized into tables using predefined schemas. NoSQL allows flexible models to be organized and horizontally scalable.

Why Use NoSQL?

Unlike relational databases, which use Structured Query Language, NoSQL databases do not have a universal query language. In fact, each NoSQL database has its own approach to query languages. Traditional relational databases will follow ACID principles, assuring a strong consistency and a structured relationship between the data.

The needs of applications have been changing through time, due to increased requirements related to big data, real-time analytics and distributed environments NoSQL emerged to satisfy:

• where scaling can be done horizontally by adding nodes instead of upgrading the existing machine.
• Flexibility in supporting unstructured or semi-structured data without a rigid schema.
• Optimized for fast read/write operations with large datasets resulting in higher performance.
• Distributed Architecture to build highly available and partition-tolerating system.

Types of NoSQL Databases

NoSQL databases are generally classified into four main categories based on how they store and retrieve data.

1. Document databases

Store data in JSON, BSON or XML format.

• Data are stored as documents that can contain varying attributes.
• Examples: MongoDB, CouchDB, Cloudant
• Ideal for content management systems, user profiles, and catalogs where flexible schemas are needed.

2. Key-value stores

• Data is stored as key-value pairs, making retrieval extremely fast.
• Optimized for caching and session storage.
• Examples: Redis, Memcached, Amazon DynamoDB
• Perfect for applications requiring session management, real-time data caching, and leaderboards.

3. Column-family stores

• Data are stored in columns rather than rows, enabling high-speed analytics and distributed computations.
• Efficient for handling large-scale data with high write/read demands.
• Examples: Apache Cassandra, HBase, Google Bigtable
• Great for time-series data, IoT applications, and big data analytics.

4. Graph databases

• Data are stored as nodes and edges, enabling complex relationship management.
• Best suited for social networks, fraud detection, and recommendation engines.
• Examples: Neo4j, Amazon Neptune, ArangoDB
• Useful for applications requiring relationship-based queries such as fraud detection and social network analysis.

Key Features of NoSQL Databases

1. Dynamic schema: Allow flexible shaping of data to meet new requirements without the need to migrate or change schemas.
   
2. Horizontal scalability: They scale horizontally for adding more nodes into the existing ones and acquire enough storage for even bigger datasets and much higher traffic by distributing the load on multiple servers.
   
3. Document-based: Data are presented in flexible, semi-structured formats like JSON/BSON (e.g., MongoDB).
   
4. Key-value-based: They possess a simple but fast access pattern (e.g., Redis) by storing data as pairs of keys and values.
   
5. Column-based: Data are organized into columns instead of rows (e.g., CASSANDRA).
   
6. Distributed and high availability: They are designed to be highly available and to automatically handle node failures and data replication across multiple nodes in a database cluster.
   
7. Flexibility: Allow developers to store and retrieve data in a flexible and dynamic manner, with support for multiple data types and changing data structures.
   
8. Performance: Perfect for big data and real-time analytics and high volume applications.

Challenges of NoSQL Databases

1. Lack of standardization:  NoSQL systems can be vastly different from one another, making it even harder to choose the right one for a specific use case.
   
2. Lack of ACID compliance: NoSQL databases may not provide consistency, which is a disadvantage for applications that need strict data integrity.
   
3. Narrow focus: Great for storage but lack functionalities as transaction management, in which relational databases are great.
   
4. Absence of Complex Query Support: They are not designed to handle complex queries, which means that they are not a good fit for applications that require complex data analysis or reporting.
   
5. Lack of maturity: Being relatively new, NoSQL may not have the reliability, security and feature set of traditional relational databases.
   
6. Management complexity: For large datasets, maintaining a NoSQL database could be quite more complicated than managing a relational database.
   
7. Limited GUI Tools: While some NoSQL databases, like MongoDB offer GUI tools like MongoDB Compass, not all NoSQL databases provide flexible or user-friendly GUI tools.
   
8. Backup: MongoDB is one of those NoSQL databases for which consistent and reliable backup is an issue.
   
9. Large document size: Databases like MongoDB and Couch DB implement JSON Documents with large sizes, contributing to speed and network performance issues.

When to Use NoSQL

Use a NoSQL database when:

• Data are unstructured or semi-structured and need a flexible schema.
• We require high scalability and must handle large datasets.
• Performance is critical for real-time applications.
• The application needs to handle distributed workloads efficiently.
• We don’t require strict ACID transactions, but prioritize availability and speed

SQL vs. NoSQL: When to use What

Popular NoSQL Databases & Their Use Cases

Conclusion

• NoSQL databases are flexible, scalable and high-performance alternative to relational databases.
• Make modern applications such as real-time analytics, big data processing and web applications more suitable for maintaining complex requirements.
• Unfortunately, with their benefits come several trade-offs, including a lack of ACID (atomicity, consistency, isolation, durability) compliance and more complex management.