What is NoSQL? Non-Relational Databases Explained
NoSQL explained
To better understand NoSQL databases, let's first take a look at their alternative: relational databases. The Structured Query Language (SQL) programming language was designed to query and modify relational databases. It became popular in the early 1970s when data storage was extremely expensive. To save on cost, data duplication between tables was minimized, resulting in a highly organized but inflexible system that was difficult to modify and required substantial design effort before the database was created.
The emergence of NoSQL
The cost of storage has plummeted since the development of SQL, making it far less important to focus on eliminating data duplication. Instead, NoSQL databases were created to improve developer productivity. They offer greater flexibility by allowing various data types to be stored together without the rigid table structure of SQL databases.
The explosion of data volume and variety from the internet has also exposed the limitations of relational databases.,Rigid, tabular relational databases are no longer a good fit for the ever-expanding variety of data sources. These sources range from internet interactions, mobile devices, and e-commerce transactions to social media, video, audio, images, sensor data, analytics, and more. Pioneered by companies like Amazon, Google, and Facebook, NoSQL, provided a new way to handle structured and unstructured data without relying on relational databases.
Scalability of NoSQL
With the rise of cloud computing and big data, many organizations deliver large-scale, personalized applications, and NoSQL the preferred technology for powering these systems.
As illustrated below, scalability is one of the key differences between relational and NoSQL databases. Relational databases typically scale vertically by upgrading hardware, such as adding RAM, CPU, or SSD, which can be costly. In contrast, NoSQL databases scale horizontally by adding commodity servers or cloud instances, allowing for more cost-effective expansion:
NoSQL vs relational databases
NoSQL and Relational Database Management Systems (RDBMS) (such as SQL) support different application requirements and frequently co-exist in enterprises to support different use cases. The key technological decision criteria include the following:
| | Relational databases | NoSQL |
|---|---|---|
| Use Case | Centralized, monolithic applications | Decentralized (highly scalable), microservice applications |
| Availability | Moderate to high availability | Continuous availability, zero-downtime |
| Speed | Moderate velocity data | High-velocity data (devices, sensors, etc.) |
| Data Types | Primarily structured data | Structured, semi-structured, or unstructured |
| Transactions | Complex/nested transactions and joins | Simple transactions and queries |
| Read + Write | Scaling reads | Scaling both writes and reads |
| Scalability | Scaling up (aka “vertical scalability”) | Scaling out (aka “horizontal scalability”) |
Different types of NoSQL databases
NoSQL also has several database types, giving you the flexibility to choose what's the best fit for your data and objectives. The main types are:
Key-value databases
Key-value databases are some of the least complex NoSQL databases, as all their data consists of an indexed key and a value. They use a hashing mechanism such that given a key, the database can quickly retrieve an associated value. Hashing mechanisms provide constant time access, which means they maintain high performance even at large scale. The keys can be any type of object, but are typically a string. The values are generally opaque blobs (i.e., a sequence of bytes that the database does not interpret). They make it easy to store large amounts of data and quickly perform lookup queries.
Examples of key-value databases include:
- Redis
- Amazon DynamoDB
- Riak
- Oracle NoSQL
Some tabular NoSQL databases, like Cassandra, can also service key-value needs.
Document databases
Document databases expand on the basic idea of key-value stores where “documents” are more complex, in that they contain data and each document is assigned a unique key, which is used to retrieve the document. These are designed for storing, retrieving, and managing document-oriented information, often stored as JSON. Each document can contain different types of data. Groups of documents are called collections. Each document in a collection can have a different structure.
Since the document database can inspect the document contents, the database can perform some additional retrieval processing. Unlike RDBMS, which require a static schema, document databases have a flexible schema as defined by the document contents.
Examples of document databases include:
- MongoDB
- CouchDB
Note that some RDBMS and NoSQL databases outside of pure document stores are able to store and query JSON documents, including Cassandra.
Tabular databases
Tabular databases organize data in rows and columns, but with a twist from the traditional RDBMS. Also known as wide-column stores or partitioned row stores, they provide the option to organize related rows in partitions that are stored together on the same replicas to allow fast queries.
Unlike RDBMSs, the tabular format is not necessarily strict. For example, Apache Cassandra™ does not require all rows to contain values for all columns in the table. Like key-value and document databases, tabular databases use hashing to retrieve rows from the table.
Examples of tabular databases include:
- Cassandra
- HBase
- Google Bigtable
Graph databases
Graph databases store their data using a graph metaphor to exploit the relationships between data. Nodes in the graph represent data items, and edges represent the relationships between the data items. Graph databases are designed for highly complex and connected data, which outpaces the relationship and join capabilities of an RDBMS. Graph databases are often exceptionally good at finding commonalities and anomalies among large data sets.
Examples of graph databases include:
- DataStax Graph
- Neo4J
- JanusGraph
- Amazon Neptune
Multi-model databases
Multi-model databases are an emerging trend in both the NoSQL and RDBMS markets. They are designed to support multiple data models against a single, integrated backend. Most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated. By contrast, a multi-model database allows an enterprise to store parts of the system's data in different data models, simplifying application development.
What are the benefits of NoSQL?
NoSQL databases are primarily designed for supporting decentralized systems that target cloud applications. A NoSQL database like Cassandra typically offers the following benefits over other database management systems:
| Benefit | Explanation |
|---|---|
| Flexibility | Doesn't require a predefined schema. New data types and fields can be added on the fly. |
| Continuously available | A database that stays online even in the face of the most devastating infrastructure outages. |
| Geographically distributed | Fully active data, everywhere you need it. |
| Operationally low latency | Response times fast enough for your most intense operational cloud applications. |
| Linearly scalable | Predictably scale out and scale in to meet the current and future data needs of cloud applications. |
| Functionally cohesive | Coherent integration and interoperability of mixed workloads and multiple data models. |
| Operationally mature | Enterprise-ready data management for cloud applications. |
| Low TCO | No requirements for specialized hardware or ancillary software. |
Enterprise use cases for NoSQL databases
NoSQL databases power modern applications that demand scalability, real-time processing, and high availability. Their flexibility in handling structured, semi-structured, and unstructured data makes them ideal for a wide range of enterprise applications. Here are some key use cases:
Session and content management
Web applications rely on NoSQL databases to efficiently manage user sessions, ensuring a responsive user experience even under heavy traffic loads. They are also widely used in content management systems (CMS) and search applications to store and manage vast amounts of semi-structured and unstructured data, such as documents, images, and videos, enabling high-speed retrieval and efficient query performance for large datasets.
Operational analytics and business analytics
Retailers and enterprises use NoSQL to unify customer data across multiple channels, from support tickets to social media to transaction history, creating comprehensive customer profiles that drive better decision-making and personalized engagement. Additionally, NoSQL databases support Online Analytical Processing (OLAP) applications, enabling complex queries and real-time analytics for larger datasets.
Real-time personalization and recommendations
E-commerce and streaming platforms use NoSQL to analyze user behavior in real-time and deliver personalized experiences. NoSQL solutions like Astra DB enable low-latency personalization at scale, enabling businesses to recommend products, movies, and other content dynamically.
Internet of things (IoT) and time-series data processing
NoSQL databases efficiently handle massive streams of IoT sensor data, tracking everything from smart home devices to industrial machinery. Their ability to scale dynamically ensures always-on availability, real-time analytics, and reliable event processing.
Fraud detection and risk management
Financial institutions rely on NoSQL for real-time anomaly detection in transactions, helping to prevent fraud before it happens. By providing efficient access to large volumes of structured and unstructured data, NoSQL databases provide institutions with the speed and scalability needed for real-time risk assessment.
AI and machine learning data pipelines
Machine learning models require fast access to diverse and evolving data sources. NoSQL databases streamline ingestion, storage, and retrieval of structured and unstructured data, making them ideal for AI-driven applications that depend on high-performance, distributed computing.
Options for implementing NoSQL
From a practical perspective, how do you go about actually moving to NoSQL and implementing your first application? In general, there are three ways to approach your adoption of a NoSQL database:
Option #1: New applications
Many begin with NoSQL by applying it in new cloud applications and starting from the ground up. This approach avoids the pain of application rewrites and data migrations.
Option #2: Augmentation
Some choose to augment an existing system by adding a NoSQL component to it. This often happens with applications that have outgrown an RDBMS due to scale problems or the need for better availability.
Option #3: Full rip-replace
For systems that exhibit growing costs, or are breaking in major ways due to increased user concurrency, data velocity, or data volume, a full replacement is done with a NoSQL database.
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