What is NoSQL?: A Complete Guide for 2025

NoSQL (also known as “no SQL” or "Not Only SQL") refers to a broad category of distributed, non-relational database systems designed for flexibility, scalability, and performance.

Unlike a traditional relational database approach, NoSQL databases allow developers to work with data closer to the application. With a NoSQL database, data is stored in a way that enables quick reads and writes, even under heavy load, making it suitable for environments where performance is the primary requirement.

NoSQL databases typically have:

• Flexible schema: NoSQL databases use dynamic or schema-less data models. Each record (such as a document or key-value pair) can have its own structure, making it easy to store data with varying attributes and to adapt the schema as requirements evolve. SQL-based databases, in contrast, enforce predefined tables and columns.
• Horizontal scalability: NoSQL systems are highly scalable and typically scale horizontally by distributing data across multiple servers or nodes in a cluster. They are designed for sharding, allowing them to handle huge volumes of data and traffic by adding more machines rather than scaling up a single machine.
• High performance: Many NoSQL databases prioritize real-time performance with simple data access patterns. Especially with the support of in-memory storage, many NoSQL databases can deliver fast queries on even large datasets.
• Multiple data models: The NoSQL category includes many data models beyond relational tables. Common types include document stores, key-value stores, wide-column stores, graph databases, time-series, and vector databases. This multi-model flexibility allows developers to choose a natural representation for their data.

The unique features of NoSQL databases are even clearer when compared with SQL databases.

Differences between SQL and NoSQL databases

Traditional SQL databases organize data into structured tables with fixed schemas, enforce ACID transactions, and use SQL as their query language. These databases tend to excel at complex joins, strict consistency, and relational integrity. NoSQL instead uses denormalized or aggregated data representations that are optimized for speed, flexibility, and scalability.

For example, if an application is retrieving a user’s profile and preferences, that process might require joining multiple tables in SQL if you’re using a traditional database, but in a NoSQL document store, the same data could be embedded in a single JSON document.

NoSQL databases are typically distributed systems where several machines work together in clusters. Each piece of data is replicated over those machines to deliver redundancy and high availability. The ability to store vast amounts of data in a distributed manner makes NoSQL databases faster to develop and deploy.

Why NoSQL matters in the AI era

According to recent research, about 181 zettabytes of data will be generated in 2025, and about 402.74 million terabytes of data are created each day. A single zettabyte equals one sextillion bytes (1,000,000,000,000,000,000,000 bytes), or the equivalent storage capacity of 250 billion DVDs.

Predicting anything in the early innings of the AI era can seem foolish, but if we zoom out far enough, trends like these become clear: The amount of data generated every year, month, and day is growing exponentially, and there’s no sign it will slow down.

This growth, however, isn’t just fueled by one technology or industry that might unexpectedly change. Here, data includes anything from user-generated content and IoT sensor data to synthetic data and AI-generated data. The volume of data will increase as the sources of data increase and as the sources diversify, so will the variety of data types and formats.

Already, traditional relational databases struggle with this volume and variety. Traditional databases are rigid, and as volumes rise, this rigidity eventually leads to fragility.

NoSQL databases thrive because they can ingest and store data without upfront schema design. The flexible, distributed nature of NoSQL is ideal for capturing the diverse, high-velocity data that is being generated in ever greater volumes. Organizations can scale out their NoSQL clusters across many nodes to handle all this data, ensuring that storage and throughput can keep up with the deluge.

Unlike traditional databases, which can be rigid until they break, NoSQL’s schema flexibility allows new data attributes or entirely new data types (such as vector embeddings or AI model outputs) to be added without requiring months of schema migrations.

AI requirements push this flexibility to the forefront. LLMs often need to store and retrieve high-dimensional feature vectors (i.e., embeddings) for tasks like semantic search, recommendations, and image recognition. Specialized vector search engines (many of which are built on NoSQL databases) can store these embeddings and perform efficient similarity searches.

This flexibility also extends to supporting data wherever it’s processed. Edge computing – when systems process data closer to where it’s generated, such as with IoT devices and branch offices – tends to do best when supported by NoSQL.

In distributed environments, data must be stored and replicated across many locations, sometimes with intermittent connectivity. NoSQL databases can provide active-active replication and partition-tolerant architectures, which are well-suited for processing at the edge – an especially useful feature for use cases like on-device machine learning and local anomaly detection.

Use cases like these benefit from fast local reads/writes and periodic syncs to the cloud. NoSQL provides eventual consistency and partition tolerance that allows an edge node to continue operating with local reads/writes even if temporarily offline, syncing up later.

AI isn’t just about flexibility, however. If you want to build LLM-powered chatbots or multi-agent systems, your database must also support speed. This is why AI systems benefit from caching layers that can store recent model inferences or conversation states. Semantic caching of AI responses can reduce the number of redundant calls to LLMs, cutting down on costs and latency.

Redis, for example, integrates vector indexing to support use cases like real-time document retrieval in RAG applications. When implemented with NoSQL stores, this technique can cut up to 30% of API calls to your LLM by serving results from cache when queries have similar meanings.

NoSQL vs SQL: Choosing the right database

At a high level, the primary difference between NoSQL and SQL is that NoSQL is a non-relational database management system, and SQL is a relational database management system.

Traditional databases, which use the relational model, use a structured schema, and data is organized into tables with rows and columns. NoSQL, in contrast, doesn’t have a fixed schema and is designed to handle unstructured and semi-structured data. The differences below flow from that core difference:

• Scalability: NoSQL is designed for horizontal scaling by sharding and clustering across many servers. SQL traditionally scales vertically, by using bigger servers, faster storage, or more powerful CPUs on a single machine.
• Performance: NoSQL excels in performance for specific access patterns, particularly situations that involve simple lookup and write operations on large volumes of data. Relational, SQL-based databases perform better in situations that involve complex querying and aggregations, such as business intelligence, reporting, and ad-hoc analysis.
• BASE vs. ACID: NoSQL systems tend to adopt a BASE approach (Basically Available, Soft state, Eventual consistency), which prioritizes systems being always available, even if some nodes are down, and tolerating temporarily inconsistent data. SQL-based systems tend to favor ACID (Atomicity, Consistency, Isolation, Durability), which prioritizes all transactions succeeding or failing as one unit.
• Query languages: There is no single query language across NoSQL databases. Redis primarily uses commands rather than a declarative language; MongoDB uses a JSON-style query syntax; and Neo4j uses Cypher for graph traversal. Relational databases standardize the usage of SQL, which means the ability to use one database can transfer to another, such as from MySQL to Postgres to Oracle.
• Structured vs. unstructured: SQL requires structured data, whereas NoSQL can handle unstructured data, which has become especially prevalent with AI.

Overall, SQL databases tend to be used for transactional systems and applications that require complex queries and relationships between multiple data sets. NoSQL databases are typically used for big data and real-time web applications, and are often used in conjunction with big data tools.

Types of NoSQL databases

There are seven major NoSQL database types: key-value stores, document databases, columnar databases, column-family databases, graph databases, vector databases, and multi-model databases.

Key-value stores

Key-value stores are the least complex of the NoSQL databases. These stores are collections of key-value pairs, and their simplicity makes them the most scalable of the NoSQL database types. Key-values can be a string, a number, or an entirely new set of key-value pairs encapsulated in an object.

Use cases include session management (e.g., storing user session state by session ID), caching (i.e., storing the results of expensive computations or database queries to serve future requests faster), and real-time leaderboards (e.g., a gaming platform that keeps a sorted set to quickly update and retrieve ranks).

Redis, for example, provides an open-source key-value store known for its versatility and performance, which is often used as an in-memory cache as well as a primary database for ephemeral or fast-changing data. Redis supports various data structures beyond plain string values, including hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, and more.

Document stores

Document stores appear the most natural among the NoSQL database types because they store everyday documents. They allow for complex querying and calculations on this often already aggregated form of data. Document databases store data in CML, YAML, JSON, or binary documents such as BSON.

Use cases include content management systems (e.g., storing articles or posts with varying metadata), ecommerce catalogs (e.g., storing an entire product with descriptions, specs, and reviews in one document), and mobile application backends (where JSON is popular and being able to store and retrieve JSON objects directly is convenient).

Redis, for example, can store data in memory as JSON objects, which, when combined with search, enables Redis to be used as a document database. MongoDB, a NoSQL database, is often less developer-friendly, requiring users to insert JSON-like documents that can then be stored on disk.

In Redis, developers can query JSON by fields if they create an index using RediSearch, allowing them to query across JSON docs for those with a certain field value. As a result, Redis tends to return results faster, because it’s all in-memory, no disk I/O, whereas MongoDB can often scale to larger dataset sizes for primary data storage, where latency is acceptable.

Columnar databases

Columnar databases store data by columns instead of by rows. Columnar storage is highly optimized for analytical queries that perform aggregates (such as SUM, AVG, and COUNT) over large datasets, or need to scan many rows but only a few columns.

By storing each column’s values contiguously, columnar databases can read data for one column quickly and skip irrelevant columns entirely for given queries. Columnar storage is optimized for analytical and business intelligence (BI) workloads, which is why many modern data warehouses and analytic databases rely on it.

Columnar databases power numerous cloud data warehouses and AI/ML data pipelines, and are often good options for large-scale aggregations, reporting, and Online Analytical Processing (OLAP) use cases. Popular options include Apache Parquet, Google BigQuery, Amazon Redshift, Snowflake, and ClickHouse.

Column-family databases

Column-family databases (also known as wide-column stores) are a type of NoSQL database that emerged from Google’s Bigtable paper and became popular through Apache Cassandra and HBase. They store data in tables, rows, and dynamic columns, but unlike relational tables, each row can have a variable number of columns.

In the 2010s, column-family databases were often the go-to solution for use cases like time-series data and high-write scenarios like logging. Since then, specialized systems, such as databases focusing entirely on time-series data, as well as improvements in other NoSQL categories, have made column-family databases less popular.

Redis treats time series as a native data structure, allowing for the ingestion and querying of millions of samples and events – all at high speeds.

Graph databases

A graph database is the most complex data store, and it’s geared toward efficiently storing relations between entities. Graph databases are designed for data that is all about relationships. They store entities as nodes and the relationships between them as edges. Each node can have properties, and each edge can have properties as well as a type or direction.

This structure directly models real-world networks, such as social networks, transportation routes, and recommendations. The power of graph databases comes from traversal, i.e., the ability to efficiently explore the network of connections.

Graph databases are often the best choice when the data is highly interconnected. Use cases include fraud detection, social networks, and knowledge graphs.

Vector databases

Vector databases are databases optimized for storing and querying vector embeddings, which are numerical representations of data that encode semantic meaning. With the rise of generative AI, it’s become common to convert unstructured data (such as text, images, and audio) into high-dimensional vectors using machine learning models.

These vectors capture similarity such that items with similar content have embeddings that are close together in vector space. Vector databases store millions or billions of these embeddings and allow efficient similarity search and other vector operations.

Vector databases are essential for applications like semantic search, recommendation systems, anomaly detection, and Retrieval-Augmented Generation (RAG). Redis, for example,

supported vector data types and vector search capabilities before AI became such a sought-after technology, which positions those features to support real-time speeds, even for intensive AI workloads.

Multi-model databases

Multi-model databases are systems that support multiple types of data models under one roof. Instead of using separate specialized databases, a multi-model database allows you to store and work with different data structures and query languages in a single platform. As data continues to grow, developers tend to prefer databases that can allow them to effectively handle diverse requirements, rather than switching from database to database.

Examples of multi-modal databases include Microsoft Azure Cosmos DB, Oracle, and Redis. Redis started as a key-value database, but has added JSON documents, full-text search, graph, time-series, streams, and vector indexing. Redis, traditionally known as a cache, has evolved into a unified data platform that allows developers to use it as a document store, a full-text search engine, a time series database, a graph database, a vector database, and a streaming engine.

NoSQL use cases

Traditional databases remain popular, but NoSQL databases are often the secret sauce behind the speed and scalability of many modern applications and features.

Real-time personalization

Delivering personalized content or recommendations instantly to users is a complex task if you attempt it with a traditional relational database. NoSQL, especially with in-memory stores, accelerates this work by storing data in a way that it can be fetched with a single key lookup or a small number of operations.

For example, an ecommerce site can keep a user’s profile, preferences, and recent activity in a single hash or document in Redis or another in-memory store. When the user visits, the application does a sub-millisecond lookup to Redis to retrieve data that can inform personalized recommendations or content feeds.

AI agent memory and inference caching

Traditional databases are not well-suited for storing ephemeral AI conversation context or intermediate inference results because using those results requires extremely fast access and frequent updates as the conversation evolves. NoSQL, particularly in-memory stores like Redis, fits better, supporting these functions as the “brain” of an AI agent that needs short-term and long-term memory.

High-velocity data ingestion

NoSQL databases are designed to ingest massive volumes of writes without choking, which is essential in scenarios like IoT, telemetry, clickstreams, and financial data. A traditional database can struggle when confronted with tens or hundreds of thousands of writes per second, because of the overhead involved in processing transactions and normalized updates.

In contrast, many NoSQL systems use append-only designs, eventually consistent replication, and sharding to linearly scale write throughput. Financial institutions, for example, often use NoSQL for fraud detection pipelines that process millions of events per second.

Operational caching for modern applications

In modern application architectures, a cache layer is indispensable for achieving and maintaining low latency as well as for offloading expensive operations from the primary database.

Redis, for example, can reduce backend load time across a range of applications, including retail or SaaS. An ecommerce store might store user session data (shopping cart, login state, etc.) live in a fast store like Redis so that each request can quickly fetch session info by session ID, rather than routing all the way to a user database. This speeds up the response time and relieves the main database from handling these frequent reads.

NoSQL for AI and Machine Learning

The rise of AI and the parallel increase in data produced every day, which will only increase faster with AI adoption, positions NoSQL databases as a critical technology for teams wanting to use AI to its maximum potential.

Vector search and similarity matching

NoSQL databases, especially those with specialized vector capabilities, are well-suited to store vector embeddings, meaning they’re well-positioned to support AI and ML use cases. In a vector database or a multi-model NoSQL database, you can optimize the storage so that searches don’t require brute-forcing through all vectors.

This efficiency allows for recommendations made in real-time and performant semantic search. Redis, for example, has been benchmarked against other database options and shown to be faster than any pure vector database providers and faster across all data sizes than general-purpose databases. In particular, Redis supports feature stores, which can manage numeric values (e.g., counts, prices), categorical features (e.g., SKUs, countries), and vector embeddings — not just similarity search.

Real-time inference and caching

AI models can be expensive and slow to run. NoSQL databases help support an architecture that can alleviate these issues by using caching and streaming mechanisms involved in the inference process.

If you have an application using an LLM to answer questions, for example, it’s wasteful to invoke the model each time and pay for each answer if questions tend to be repetitive. By caching LLM outputs, including tokens and sessions, you can serve repeated queries straight from the cache. A NoSQL store is ideal for this because of the need for fast writes and fast reads.

Maintaining this level of performance requires sub-millisecond response requirements, however, which only a few databases can support. This is why Redis Streams can act as a high-speed buffer for ingesting real-time signals into live AI models for use cases like anomaly detection and real-time personalization.

AI agent memory systems

AI agents, including virtual assistants, chatbots, and autonomous agents, require memory to function effectively, and they need both short-term memory (working memory of the current conversation or task) and long-term memory (knowledge or context they’ve learned or been given over time). NoSQL databases are frequently used to implement these memory components because of their real-time read/write abilities and flexible data models.

For example, if an AI agent is using a tool and gets an answer, it might store that intermediate answer in a memory buffer that it can reference later. NoSQL stores like Redis provide that ultra-fast read/write functionality so the agent can log each turn or result as it happens.

Redis: The leading NoSQL solution

Especially given the explosion in data produced every year and the AI forefront that advances every day, Redis stands out as the leading NoSQL database. Numerous features make it a leading choice for companies looking to manage more data in the face of AI, including:

• Multi-model architecture: Redis unifies multiple data models into one developer-friendly platform, natively supporting JSON for document storage and querying, vector search for AI and semantic similarity, Time series for analytics and observability, and streams for event-driven architectures and real-time messaging.
• Performance at scale: Redis provides sub-millisecond response times reliably and at production scale, giving users the ability to linearly scale to 250 million operations per second and rely on 99.999% uptime guarantees.
• Enterprise-ready features: Redis provides active-active geo-replication across regions, automatic failover and high availability, data persistence options and backup strategies, and security, compliance, and audit capabilities – all of which are even more important given the nondeterminative nature of AI.
• Developer experience: Redis offers rich client libraries across programming languages, Redis OM object mapping for easier development, Redis Insight for monitoring and debugging, RDI for easy CDC and data integration, and extensive documentation and community support, which all, when combined, make Redis one of the most developer-friendly database options available.

To get started with NoSQL and Redis, try Redis for free today or schedule a personalized demo.