What Is a NoSQL Database? - Klaviyo
2026-03-10 23:29 Diff
https://www.klaviyo.com/glossary/what-is-a-nosql-database

A NoSQL database is a type of database that stores and retrieves data more flexibly and extensibly than traditional relational databases, which use Structured Query Language (SQL). Instead of organizing information into rigid tables with rows and columns, NoSQL databases (short for "not only SQL") use flexible data structures like documents, key-value pairs, wide columns, or graphs. This flexibility makes them well-suited for handling large volumes of the kind of unstructured or semi-structured data that B2C brands generate every day.

Whereas traditional relational databases require a fixed schema, meaning you have to define your data structure upfront, NoSQL databases don't have that limitation. You can add new data types as needed, store customer information in whatever format makes sense, and query it all in real time. That means NoSQL databases handle the speed and scale that personalized B2C customer experiences demand.

Think about what happens when a customer browses your site, adds items to their cart, chats with support, and then makes a purchase. Each of those interactions generates data in different formats. A NoSQL database can store all of it—browsing behavior, cart contents, chat transcripts, order details—without forcing you to restructure your entire database every time you want to capture something new. That's the kind of flexibility that powers real-time personalization at scale.

Why does a NoSQL database matter?

The variety and volume of B2C customer data makes it difficult to manage. People interact with brands across dozens of touchpoints—websites, mobile apps, email, text messaging, in-store visits, social media, customer service chats—and each interaction generates data in a different format.

Traditional relational databases struggle with this variety because they require data to conform to predefined structures. NoSQL databases help address this problem by accepting data in whatever shape it arrives. They’re built for horizontal scalability, meaning you can add more servers to handle increased load instead of upgrading a single expensive machine. This makes them well-suited for applications that need to process millions of customer interactions in real time.

For B2C brands, NoSQL databases store event data from web tracking, mobile apps, point-of-sale systems, and more, then make it available for personalization efforts, all without waiting for a data engineer to modify the schema first.

Benefits of NoSQL databases

NoSQL databases deliver practical advantages for businesses dealing with large-scale customer data, such as:

  • Faster time to market: Development teams can iterate more quickly without rigid schema requirements, often shipping new features with fewer schema migrations and less downtime.
  • Lower infrastructure costs: Horizontal scaling on commodity hardware can help control infrastructure spend compared to relying solely on vertical scaling on specialized servers.
  • Real-time personalization at scale: Flexible data models and fast query performance allow you to personalize customer experiences in real time, even with millions of profiles and high event volumes.
  • Simplified data integration: Flexible schemas can make it easier to build data pipelines that ingest data from multiple sources—ecommerce platforms, point-of-sale systems, mobile apps, customer service tools, and more—without extensive data transformation.
  • Resilience during peak traffic: Built-in replication and distribution can help your database handle traffic spikes during high-volume periods like Black Friday Cyber Monday (BFCM) with less risk of degraded performance.

NoSQL vs. SQL databases

SQL databases may be the best choice when you need strict data integrity and the ability to join data across multiple tables. They’re often well-suited for financial systems, inventory management, and other applications where consistency is paramount.

NoSQL databases may be the best choice when you need flexibility, horizontal scalability, and fast performance with large volumes of varied data. They’re often well-suited for customer data platforms, real-time personalization, and applications that need to evolve quickly without schema migrations.


Characteristic


SQL databases


NoSQL databases

Data structure

Fixed schema with tables, rows, and columns

Flexible schema with documents, key-value pairs, or graphs

Scalability

Vertical (add more power to one server)

Horizontal (add more servers to the cluster)

Query language

Standardized SQL

Varies by database type

Data relationships

Strong support for joins across tables

Limited or no join support, with relationships handled differently

Best for

Complex transactions, structured data, reporting

Large-scale applications, unstructured data, real-time processing

Key features of NoSQL databases

NoSQL databases share several characteristics that help solve B2C data challenges. Here are a few core features that define how they work:

  • Flexible schemas: You can add new fields or data types without modifying existing structures, allowing your data model to evolve as your business needs change.
  • Horizontal scalability: You can distribute data across multiple servers through sharding and replication, adding more machines to the cluster rather than upgrading a single server.
  • High availability: Built-in replication keeps copies of your data across multiple nodes so that if one server goes down, others can continue serving requests with minimal disruption.
  • Fast read and write performance: These databases are optimized for speed, especially for simple queries that don't require complex joins.
  • Support for unstructured data: You can store documents, images, JavaScript Object Notation (JSON) objects, and other data types that don't fit neatly into rows and columns.

How to choose and implement a NoSQL database

A successful implementation typically begins with a rigorous analysis of your data needs and includes optimizing query performance for real-time speed. Here’s where to start:

Step 1: Assess your application's data requirements

Before selecting a specific technology, evaluate the volume, variety, and velocity of your data to confirm that a NoSQL database aligns with your scalability goals. This involves analyzing whether your data is unstructured, if your schema needs to change frequently, and if you anticipate traffic spikes that require horizontal scaling—all critical components of a data-driven marketing strategy.

Use the following success criteria to decide whether NoSQL is a good fit:

  • High velocity: You need to ingest thousands of events per second.
  • Variable data: Your data structure varies by customer or product line, making rigid SQL tables impractical.
  • Massive scale: You anticipate data growth exceeding the capacity of a single server (vertical scaling limits).
  • Agile development: Your team needs to iterate on features quickly without downtime for schema migrations.

Step 2: Select the appropriate NoSQL data model

Not all NoSQL databases are interchangeable. Match your application's specific function to the data model optimized for that workload. Consider the following model-to-use-case guidance:

  • Document databases: These store data as JavaScript Object Notation (JSON)-like documents, making them ideal for customer profiles, product catalogs, and content management.
  • Key-value databases: These map unique keys to values and are efficient for caching, session storage, and storing user preferences where you need quick lookups by a single identifier.
  • Wide-column databases: These organize data into tables with rows and dynamic columns, where each row can have different columns, and are best for analyzing massive datasets, time-series data, and event logging across distributed systems.
  • Graph databases: These model data as nodes and edges to emphasize relationships between entities, making them powerful for recommendation engines and fraud detection.
  • In-memory databases: These keep data in Random Access Memory (RAM) rather than on disk for extremely low latency, commonly used for real-time analytics.

Step 3: Design your data structures for performance

While NoSQL offers schema flexibility, you should intentionally design your data model to optimize for read speeds rather than storage efficiency. This usually involves "denormalization," where you embed related data directly within a single document or record to avoid complex, slow joins during query time.

Use the following best practices for NoSQL data modeling:

  • Embed for read speed: If you frequently access customer addresses with their profile, store the address inside the customer document rather than in a separate table.
  • Reference for write speed: If data changes frequently and is used by many documents (like product pricing), keep it separate and reference it to avoid updating thousands of records at once.
  • Design for access patterns: Model your data based on how it will be queried by the application, not just how the data relates to itself.

Step 4: Set up your database environment

Configure your environment to handle horizontal scalability and high availability. This involves setting up "sharding" to distribute data across multiple servers and configuring replication so that if one node fails, another can take over quickly with minimal disruption, depending on your replication and durability settings.

Step 5: Implement and optimize data queries

Querying non-relational data differs significantly from standard SQL, often requiring you to define specific access patterns and indexes upfront. Create compound indexes that match your most frequent queries so the database can retrieve specific documents quickly without scanning the entire collection.

Here are a few common indexing strategies:

  • Single field index: This speeds up queries that filter by a specific unique identifier, such as an email address or product SKU.
  • Compound index: This is necessary for queries that filter by multiple criteria, such as finding users who are both "active" AND located in "New York."
  • Time-to-live (TTL) index: This automatically deletes data after a set period, which is useful for managing temporary session data or logs.

NoSQL databases can give B2C brands the flexibility and scale they need to help build unified customer profiles and deliver personalized experiences through omnichannel marketing.

Ready to put your customer data to work? Get started today with Klaviyo Data Platform, the customer data platform built in to Klaviyo B2C CRM.