NOSQL DATABASE.

A NoSQL database is a data management system that uses flexible data models to efficiently handle large volumes of unstructured and semi-structured data. Unlike traditional relational databases, which impose rigid data structures, NoSQL databases allow storing more arbitrary collections of data. This makes NoSQL databases a good option for modern applications and development workflows that require a more flexible data model and scalability. However, this flexibility often comes at the cost of consistency and transactional guarantees.

In this article, we’ll explain more about the NoSQL database model, its benefits and describe some more popular systems available today that can handle NoSQL and relational database use cases.

How does NoSQL work?

NoSQL features are unique to the database you choose. However, they typically share several similar high-level qualities: 

• They follow flexible schemas that don’t require you to determine or declare a fixed schema for your data, making them ideal for semi-structured and unstructured data
• They scale horizontally, using range or hash distributions, instead of having to depend on vertical scaling to add capacity
• They’re optimized for specific data models and workload patterns, such as key-value, wide-column, or in-memory
• They often exhibit consistency at some later point (for example, eventual consistency model) rather than following the stricter ACID (atomicity, consistency, isolation, durability) properties of relational and SQL databases
• They usually don't support cross-shard transactions or flexible isolation modes

These features make non-relational databases ideal for applications that require large scale, reliability, high availability, and frequent data changes.

Flexibility

The NoSQL family is diverse in its variety of data models, from key-value pairs, to row/column stores, and JSON documents to meet virtually any need. In addition, the majority of NoSQL databases neither require nor enforce strict schemas, enabling quicker and easier deployment of schema changes to production. Some NoSQL databases even accommodate multiple different data models for maximal flexibility.

Scalability and Uptime

Plenty of NoSQL databases are capable of distributing their work load over multiple instances, servers, nodes, etc. The ability to scale both horizontally and vertically is always a first-class feature with NoSQL databases. They can also be incredibly robust, using built-in protocols to continue operating when connections or nodes fail.

Performance

Because NoSQL databases are focused on fewer features and simpler data models, they typically outperform SQL databases to a significant degree. This specialization comes at the cost of “trying to do it all” but is a good tradeoff, given that most applications don’t utilize every database feature anyway, NoSQL or otherwise. It also accelerates NoSQL evolution insofar as developers have a smaller list of features to maintain and improve.

Query Interface

The functionality found in most NoSQL databases tends to be easier to leverage and integrate into applications, given the flexibility of data models and focus of purpose. This is particularly true for more modern architectural choices such as microservices, fully JavaScript web development, etc. As such, NoSQL databases accelerate software development compared to older technologies.

SQL vs NoSQL: What’s the difference?

The gap in functionality between NoSQL and SQL databases can be reduced essentially to a few core features. The most significant are arguably (A) how relational data can be accomodated, (B) support for ACID-compliant transactions, and (C) a widely adopted query interface for advanced data manipulation.

Modern NoSQL databases like Fauna, now tackle the lack of relational data modeling as well as the widespread desire for ACID-compliant transactions. However, the first generation NoSQL solutions like MongoDB, retain these caveats.

Data Modeling

Below are two common NoSQL data models along with SQL’s tabular data model.

While SQL databases require pre-defining tables, columns, primary keys, and more, many NoSQL databases default to enforcing few to no such requirements. For example, a document database (loosely illustrated above) can store any arbitrary data in the form of JSON. This benefits agile development, as applications can iterate on their usage of data without issues caused by schema changes.

ACID Compliance

One of the sacrifices made in early NoSQL databases was support for ACID-compliant transactions, largely due to its complexity among distributed databases. Such transactions allow reliable manipulation of data across multiple tables or databases, ACID being an acronym for Atomic, Consistent, Isolated, and Durable. For example, a transaction might involve an e-commerce purchase in which the database transaction must fail if the quantity to be purchased exceeds available inventory. ACID-compliance ensures the transaction is completed (committed) or cancelled (rolled back) in a reliable way. In case of failure, the customer is not charged, no receipts or confirmations are sent, the product is never pulled for shipment, etc. Without the atomic guarantee, it would be possible for part of the process to occur while other parts do not; e.g., charging the customer but never giving a receipt or the product.

Further explanation of ACID is out of this article’s scope, but the point is that ACID-compliance is easier to achieve with a single database server or geographically small deployment. When struggling SQL databases (which had ACID-compliant transactions in many instances) were replaced with distributions of early NoSQL databases, full ACID-compliance was abandoned in favor of “eventual consistency”. Fortunately, advancements have been made over time leading to partial ACID-compliance among many NoSQL databases, along with a few providing full ACID-compliance.

Query Interface

NoSQL may never have a single, ubiquitous interface like SQL (as a language), but there are already several NoSQL databases offering advanced data manipulation via common nascent interfaces. For example, GraphQL has gained popularity among NoSQL databases, though it isn’t intended for the same power of which SQL is capable.

What are the types of NoSQL Databases?

The most common types of NoSQL databases are document, key-value, wide-column, and graph databases. The following sections will explain them briefly.

Document databases

As the name implies, document databases store collections of ‘documents’, which typically refers to hierarchical collections of data properties/values in a JSON format. Given their flexible and forgiving nature, document databases are suitable for a variety of applications. They are particularly approachable for novice engineers and well-suited for applications that require simple CRUD (Create, Read, Update, and Delete) operations.

Key-value databases

Key-value databases offer the fastest operation of all NoSQL types of databases. They are suitable only for the simplest applications in which data is only created, read, and deleted—no complex updates allowed. For suitable use cases, key-value databases should be strongly considered as their speed and optimization are hard to beat.

Wide-column databases

Wide-column databases are similar to SQL in that they’re often designed with tables, columns, and rows. They offer excellent speed and scale by leveraging clever storage partitioning but often require more planning than other approaches, due to their reliance on developers specifying the layout/grouping of said partitions. Wide-column databases are best suited for applications that have extreme storage requirements.

Graph databases

Finally, graph databases are often used to supplement another database and excel in modeling relational data. While most relational databases can model only simple relationships with references or primary/foreign keys, graph databases utilize networks of “nodes”, connected with the use of “edges” to store significantly more complex (e.g., hierarchical) relationships.

A node is an entity while an edge is a relationship, both of which can usually store additional properties and data; enabling applications to describe “how” one entity relates to another, “in what way” two entities are related, and more. They bring immense optimization potential and open new doors for functionality when integrated with an application that utilizes any sort of network (e.g. social networks).

Examples of NoSQL Databases

NoSQL’s rapid growth over the years, in both maturation and adoption, has led to what some may describe as a NoSQL renaissance. Multi-model databases, Fauna in particular, offer the flexibility and scale of non-relational databases, along with the coveted relational and graph database functionality of relational database management systems.

AWS’s DynamoDB provides applications with quick CRUD operations and classic NoSQL scale if implemented correctly; though Fauna excels in both areas by providing one of the fastest global transaction protocols in the industry, along with true hands-free serverless scale.

MongoDB, a database especially popular among beginners, continues to provide an excellent NoSQL experience with an easy-to-use document data model. Even easier, however, are the serverless options like Fauna, which also maintain the relational capabilities of traditional SQL databases and the strongest level of data consistency.

Early NoSQL databases provided much-needed scale for the major players of the early internet. Today, databases like Fauna have only amplified that original value, providing more scale, flexibility, and state-of-the-art functionality, such as automatic distribution, an API delivery model, and a modern document-relational data model.

TOP TOOLS USED NOSQL DATABASES

1. MongoDB

MongoDB remains one of the most popular document-based NoSQL databases, offering scalability, flexibility, and strong indexing capabilities. It’s widely used for web applications, mobile apps, and big data. MongoDB supports JSON-like document storage, making it easy to use for developers. With features like sharding, replica sets, and built-in aggregation framework, it is ideal for handling large-scale applications.

Use Cases:

• E-commerce platforms
• Real-time analytics
• Content management systems

2. Apache Cassandra

Apache Cassandra is an open-source, distributed NoSQL database known for high availability and fault tolerance. It is designed for applications requiring massive scalability and high performance. With a peer-to-peer architecture and multi-data center support, it ensures that no single point of failure exists.

Use Cases:

• Large-scale distributed applications
• Time-series data storage
• Financial transaction systems

3. Amazon DynamoDB

Amazon DynamoDB is Amazon's fully managed NoSQL database that provides low-latency performance at any scale. It supports both key-value and document data models and integrates well with AWS Lambda, making it ideal for serverless architectures.

Use Cases:

• Gaming leaderboards
• IoT applications
• Mobile and web apps

4. Redis

Redis is an in-memory key-value store known for its lightning-fast performance. It supports data structures such as lists, sets, and sorted sets, making it ideal for caching, session management, and real-time analytics.

Use Cases:

• Caching solutions for web applications
• Real-time leaderboards
• Message brokering

5. Couchbase

Couchbase is a multi-model NoSQL database offering high performance, distributed architecture, and SQL-like querying capabilities. It combines the strengths of key-value and document databases with built-in mobile syncing support.

Use Cases:

• Enterprise applications
• Mobile data synchronization
• Customer profile management

6. ArangoDB

ArangoDB is a multi-model database supporting document, key-value, and graph-based data storage. It allows developers to work with different data models in a single query language, making it highly flexible for complex applications.

Use Cases:

• Fraud detection
• Recommendation engines
• Knowledge graphs

7. Neo4j

Neo4j is the leading graph database, designed to manage complex relationships between data points efficiently. It is widely used in applications requiring deep relationships and graph-based queries.

Use Cases:

• Social networks
• Fraud detection
• Network and IT operations

8. FaunaDB

FaunaDB is a globally distributed, serverless database that offers strong consistency and native GraphQL support. It is designed for modern cloud applications needing scalability and high availability.

Use Cases:

• Serverless applications
• Global-scale e-commerce
• API-driven applications

9. Google Firestore

Google Firestore is a cloud-native NoSQL database that integrates seamlessly with Firebase and Google Cloud, making it an excellent choice for mobile and web applications requiring real-time synchronization.

Use Cases:

• Mobile applications
• Real-time collaborative apps
• Chat applications

10. ScyllaDB

ScyllaDB is a high-performance NoSQL database designed as a drop-in replacement for Apache Cassandra. It provides ultra-low latency, high throughput, and better efficiency for massive workloads.

Use Cases:

• High-performance big data applications
• Real-time analytics
• Video streaming services.

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