StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Celery vs Confluent

Celery vs Confluent

OverviewComparisonAlternatives

Overview

Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K
Confluent
Confluent
Stacks337
Followers239
Votes14

Celery vs Confluent: What are the differences?

# Introduction
When choosing between Celery and Confluent for managing distributed systems and data streams, it is essential to understand the key differences between the two technologies to make an informed decision.

1. **Messaging System**: Celery is primarily a distributed task queue that relies on a messaging system such as RabbitMQ or Redis for task dispatching and monitoring. In contrast, Confluent is more focused on managing real-time data streams through Apache Kafka, offering features like data integration, real-time processing, and analytics.
   
2. **Use Case**: Celery is ideal for handling asynchronous tasks and job scheduling in applications, providing scalability and fault tolerance for distributed systems. On the other hand, Confluent is designed for processing and managing continuous streams of data, enabling real-time analytics, data pipelines, and event-driven architectures.

3. **Ease of Integration**: Celery can be seamlessly integrated with various programming languages and frameworks, making it versatile for different development environments. In comparison, Confluent provides native integration with Apache Kafka, ensuring compatibility and efficient data flow within the Kafka ecosystem.

4. **Scalability and Performance**: Celery offers horizontal scalability by adding more worker nodes to handle increased workloads efficiently. Confluent, powered by Apache Kafka, provides high throughput and low latency processing capabilities for handling massive volumes of data streams at scale.

5. **Monitoring and Management**: Celery provides built-in monitoring tools like Flower for real-time monitoring of tasks, worker nodes, and queues. In contrast, Confluent Control Center offers comprehensive monitoring and management features for tracking data pipelines, resource utilization, and performance metrics in Apache Kafka environments.

6. **Community and Support**: Celery has a vibrant open-source community and active development, providing continuous improvements and bug fixes. Confluent offers enterprise support and professional services for businesses requiring additional assistance, SLAs, and advanced features for mission-critical data streaming applications.

# In Summary, Understanding the specific use cases and requirements can help in choosing between Celery and Confluent for task processing and data streaming needs efficiently.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Celery
Celery
Confluent
Confluent

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

-
Reliable; High-performance stream data platform; Manage and organize data from different sources.
Statistics
GitHub Stars
27.5K
GitHub Stars
-
GitHub Forks
4.9K
GitHub Forks
-
Stacks
1.7K
Stacks
337
Followers
1.6K
Followers
239
Votes
280
Votes
14
Pros & Cons
Pros
  • 99
    Task queue
  • 63
    Python integration
  • 40
    Django integration
  • 30
    Scheduled Task
  • 19
    Publish/subsribe
Cons
  • 4
    Sometimes loses tasks
  • 1
    Depends on broker
Pros
  • 4
    Free for casual use
  • 3
    Dashboard for kafka insight
  • 3
    No hypercloud lock-in
  • 2
    Zero devops
  • 2
    Easily scalable
Cons
  • 1
    Proprietary
Integrations
No integrations available
Microsoft SharePoint
Microsoft SharePoint
Java
Java
Python
Python
Salesforce Sales Cloud
Salesforce Sales Cloud
Kafka Streams
Kafka Streams

What are some alternatives to Celery, Confluent?

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

IronMQ

IronMQ

An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase