Skip to content

Commit b12ce61

Browse files
AxeemHaiderAxeemHaider
committed
kafka as database streaming
1 parent cb77203 commit b12ce61

File tree

3 files changed

+154
-1
lines changed
  • content/topics/open-source-databases

3 files changed

+154
-1
lines changed

‎content/topics/open-source-databases/kafka-as-database-streaming/images/cover.png‎

2.15 MB
Loading

‎content/topics/open-source-databases/kafka-as-database-streaming/index.md‎

Lines changed: 153 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,153 @@
1+
---
2+
draft: false
3+
title: 'Kafka as a Database: When Should You Use It for Streaming Data?'
4+
date: '2025-09-20'
5+
summary: 'Apache Kafka isn’t a traditional relational or NoSQL database, but it can function as a database for streaming data. By storing events durably, enabling replay, and supporting real-time processing through Kafka Streams and ksqlDB, Kafka is ideal for event sourcing, data pipelines, and microservices communication. However, it’s not suited for transactional workloads, long-term archival, or general-purpose CRUD operations. The best approach is to use Kafka alongside open-source databases like PostgreSQL, ClickHouse, Redis, or TimescaleDB to build modern, scalable data infrastructures that balance real-time event streaming with persistent storage.'
6+
description: 'Discover when to use Kafka as a database for streaming data. Learn its use cases, limits, and how it fits with open-source databases like PostgreSQL and ClickHouse.'
7+
tags: [Kafka as a database, event streaming, real-time data pipelines, Apache Kafka, open-source databases]
8+
categories: ['Databases', 'Open-Source Hosting', 'Cloud & Infrastructure']
9+
author: 'OctaByte'
10+
cover:
11+
image: images/cover.png
12+
caption: 'Kafka as a Database: Understanding When to Use It for Streaming Data'
13+
alt: 'A flat-design infographic showing a database icon and server rack on the left, the Kafka logo in the center, and a computer monitor with an upward-trending graph on the right. The title text reads: ‘Kafka as a Database – When Should You Use It for Streaming Data?’ in bold white letters against a blue background.'
14+
relative: true
15+
ShowToc: true
16+
TocOpen: true
17+
---
18+
19+
**Direct Answer:**
20+
Apache Kafka can act as a *database for streaming data* by storing, processing, and replaying event logs in real time. While it’s not a traditional relational or NoSQL database, Kafka is ideal when your application requires **high-throughput event streaming, real-time analytics, or data pipelines** that connect multiple systems. Use Kafka as a database when you need durable, replayable event storage and fast access to continuously changing data.
21+
22+
---
23+
24+
## Introduction
25+
26+
When people hear **Apache Kafka**, they usually think of a *messaging system* or *event streaming platform*. But in recent years, more teams have started asking: **“Can Kafka be used as a database?”**
27+
28+
The short answer is *yes—but with caveats*. Kafka isn’t built to replace PostgreSQL, MySQL, or MongoDB, but it can act as a **commit log database for streaming data**. This makes it a unique piece of the **open-source database ecosystem**, especially for real-time workloads.
29+
30+
In this guide, we’ll break down **what Kafka is, how it works as a database, when you should use it, and when you shouldn’t**. We’ll also compare it with traditional databases and link it to other open-source tools you might already be using.
31+
32+
---
33+
34+
## What is Kafka and How Does It Work?
35+
36+
Apache Kafka is an **open-source event streaming platform** originally developed by LinkedIn and now maintained by the Apache Software Foundation.
37+
38+
At its core, Kafka is a **distributed commit log**. Instead of rows in tables, Kafka stores data in **topics** made of **partitions**, which can be replicated across clusters.
39+
40+
* **Producers** write data (events/messages) to topics.
41+
* **Consumers** read and process these events.
42+
* **Brokers** manage the storage and distribution of messages.
43+
44+
Kafka’s design ensures **high throughput, durability, and scalability**, making it an excellent backbone for **real-time data pipelines**.
45+
46+
---
47+
48+
## Kafka as a Database: What Does It Mean?
49+
50+
When people say **Kafka is a database**, they usually mean:
51+
52+
1. **Persistent Storage:** Kafka stores all events durably on disk, not just in memory.
53+
2. **Replayability:** Unlike message queues, Kafka allows you to replay messages at any time.
54+
3. **Stream Processing:** Tools like Kafka Streams and ksqlDB let you query and transform data in motion.
55+
4. **Event Sourcing:** Kafka can act as the single source of truth for application state.
56+
57+
In this sense, Kafka behaves more like an **append-only database of events**.
58+
59+
---
60+
61+
## When Should You Use Kafka as a Database?
62+
63+
Here are the best use cases:
64+
65+
* **Real-Time Event Streaming**
66+
Applications that rely on clickstreams, IoT sensor data, or financial transactions benefit from Kafka’s event-first model.
67+
68+
* **Data Pipelines & ETL**
69+
Kafka acts as the backbone between systems—streaming data from PostgreSQL, MySQL, or MongoDB into analytics engines like [ClickHouse](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/clickhouse).
70+
71+
* **Event Sourcing**
72+
Instead of only storing the current state, Kafka stores every change as an immutable log—great for audit trails.
73+
74+
* **Microservices Communication**
75+
Kafka provides durable, high-speed messaging for distributed systems.
76+
77+
* **Streaming Analytics**
78+
With [TimescaleDB](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/timescaledb) or [InfluxDB](https://octabyte.io/fully-managed-open-source-services/databases/specialized-databases/influxdb), you can combine Kafka for ingestion with these specialized databases for time-series queries.
79+
80+
---
81+
82+
## When Should You *Not* Use Kafka as a Database?
83+
84+
While Kafka has database-like qualities, it has **limitations**:
85+
86+
* ❌ **Not for Transactional Workloads**
87+
Kafka doesn’t support SQL joins, ACID guarantees, or relational integrity like [PostgreSQL](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/postgresql).
88+
89+
* ❌ **Not for Long-Term Archival**
90+
Kafka isn’t optimized for storing data for years—use [ClickHouse](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/clickhouse) or [MariaDB ColumnStore](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/columnstore) for that.
91+
92+
* ❌ **Not a General Purpose DB**
93+
Kafka is event-first. If you just need CRUD operations, consider [MongoDB alternatives like FerretDB](https://octabyte.io/fully-managed-open-source-services/databases/nosql/ferretdb).
94+
95+
---
96+
97+
## Kafka vs Traditional Databases
98+
99+
| Feature | Kafka | PostgreSQL/MySQL/MongoDB |
100+
| ----------------- | ---------------------------------- | ------------------------ |
101+
| **Storage Model** | Event log (append-only) | Tables & documents |
102+
| **Transactions** | Limited | Full ACID support |
103+
| **Querying** | Streams, ksqlDB | SQL / NoSQL queries |
104+
| **Retention** | Configurable, short to medium term | Long-term, persistent |
105+
| **Use Case** | Real-time streaming, pipelines | OLTP, analytics, CRUD |
106+
107+
---
108+
109+
## How Kafka Fits Into the Open-Source Database Ecosystem
110+
111+
Kafka is rarely used **alone**. It usually works alongside other open-source databases:
112+
113+
* **Kafka + PostgreSQL** → Event-driven applications with transactional storage.
114+
* **Kafka + ClickHouse** → Real-time analytics pipelines.
115+
* **Kafka + Redis** → Fast caching of Kafka streams for low-latency applications.
116+
* **Kafka + InfluxDB/TimescaleDB** → IoT and monitoring data.
117+
118+
This combination allows you to get **the best of both worlds**—durable event logs with queryable databases.
119+
120+
---
121+
122+
## Best Practices for Using Kafka as a Database
123+
124+
1. **Use Compaction for State Storage** – Kafka log compaction keeps the latest value for each key.
125+
2. **Integrate with ksqlDB or Kafka Streams** – Run real-time transformations and queries.
126+
3. **Set Proper Retention Policies** – Avoid disk bloat by managing how long data stays.
127+
4. **Pair with a Database** – For most applications, Kafka should complement, not replace, a database.
128+
129+
---
130+
131+
## FAQ
132+
133+
**Q1: Can Kafka replace PostgreSQL or MySQL?**
134+
No. Kafka is not a replacement for relational databases. It’s designed for event streaming and should be paired with databases like PostgreSQL or MySQL for transactional workloads.
135+
136+
**Q2: Is Kafka good for storing historical data?**
137+
Not really. Kafka is better for short-to-medium-term storage. For historical analytics, use [ClickHouse](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/clickhouse) or [MariaDB ColumnStore](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/columnstore).
138+
139+
**Q3: Does Kafka support SQL queries?**
140+
Yes, via **ksqlDB**, but the capabilities are limited compared to relational or NoSQL databases.
141+
142+
**Q4: What’s the main advantage of Kafka over a traditional database?**
143+
Kafka excels at **real-time data streaming, replayability, and high throughput**, which traditional databases aren’t optimized for.
144+
145+
---
146+
147+
## Conclusion
148+
149+
Kafka as a database makes sense when you need **real-time event streaming, durable commit logs, and replayable data pipelines**. However, it’s not a silver bullet—you’ll still need traditional databases like [PostgreSQL](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/postgresql), [MySQL](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/mysql), or [ClickHouse](https://octabyte.io/fully-managed-open-source-services/databases/relational-databases/clickhouse) to handle long-term, transactional, or analytical workloads.
150+
151+
By pairing Kafka with other **open-source databases**, you can build a modern, scalable data infrastructure that handles both **real-time streams and persistent storage**.
152+
153+
Want more open-source hosting insights? Don’t miss [The Ultimate Guide to Open-Source Databases (2025)](/topics/open-source-databases/ultimate-guide-2025/).

‎content/topics/open-source-databases/scylladb-use-cases-cassandra-alternative/index.md‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -46,7 +46,7 @@ IoT sensors generate **millions of time-stamped events per second**. ScyllaDB is
4646
ScyllaDB handles **high-volume, append-only workloads** well, making it a natural fit for messaging platforms or log/event storage.
4747

4848
- Example: Telecom companies handling SMS, chat, or call metadata.
49-
- Related: For real-time event pipelines, see *Kafka as a Database*
49+
- Related: For real-time event pipelines, see [Kafka as a Database](../kafka-as-database-streaming/)
5050

5151
---
5252

0 commit comments

Comments
 (0)