---
title: "Five Common Data Stores and When to Use Them"
description: "The article compares five common data stores—relational databases, NoSQL databases, key-value stores, full-text search engines, and message queues—and connects each to appropriate design needs. Relational databases use schema-bound tables, primary and foreign keys, SQL, and ACID transactions for durable business-critical data, while NoSQL databases trade strict schemas for flexibility with document and wide-column models. In-memory stores such as Redis and Memcached provide fast access and configurable eviction for caching; Redis additionally supports richer data structures and disk persistence. Full-text search engines target word and substring searches, whereas Kafka uses a distributed append-only log and producer-consumer model to move data reliably between services. The article concludes that the right choice depends on whether data must be permanent, flexible, quickly accessed, searched, or transferred."
---

# Five Common Data Stores and When to Use Them

[Shopify](https://yomu.fyi/company/shopify) · 2023-10-18 · Oct 15, 2019

**Type:** Explainer

## Summary

The article compares five common data stores—relational databases, NoSQL databases, key-value stores, full-text search engines, and message queues—and connects each to appropriate design needs. Relational databases use schema-bound tables, primary and foreign keys, SQL, and ACID transactions for durable business-critical data, while NoSQL databases trade strict schemas for flexibility with document and wide-column models. In-memory stores such as Redis and Memcached provide fast access and configurable eviction for caching; Redis additionally supports richer data structures and disk persistence. Full-text search engines target word and substring searches, whereas Kafka uses a distributed append-only log and producer-consumer model to move data reliably between services. The article concludes that the right choice depends on whether data must be permanent, flexible, quickly accessed, searched, or transferred.

## Context

Choosing a data store is a technical-design decision shaped by whether data has a fixed or flexible schema, whether it must persist permanently, and whether it needs to be temporarily stored, searched, or transferred.

## Approach / What changed

The article compares relational databases, NoSQL databases, key-value stores, full-text search engines, and message queues by describing their data models, durability, performance, persistence, scalability, and suitable use cases, with examples including MySQL, Bigtable, Redis, Memcached, Elasticsearch, and Kafka.

## Takeaways

- Relational databases enforce schemas and support primary keys, foreign keys, SQL, and ACID transactions, making them suited to durable, business-critical information.
- Redis and Memcached are in-memory key-value stores with configurable eviction policies; Redis also supports native data structures and optional disk persistence, while Memcached is limited to caching.
- Kafka is a distributed append-only log with producer-consumer access, making it useful for reliably moving data between services and scaling through distributed partitions.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Databases](https://yomu.fyi/topic/databases), [Kafka](https://yomu.fyi/topic/kafka), [Redis](https://yomu.fyi/topic/redis), [Search](https://yomu.fyi/topic/search)

- Source: [Shopify](https://shopify.engineering/five-common-data-stores-usage)
- Source URL: https://shopify.engineering/five-common-data-stores-usage
- Ingested by Yomu: 2026-08-31T01:13:44.485Z

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