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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Apache NiFi vs Celery

Apache NiFi vs Celery

OverviewComparisonAlternatives

Overview

Celery
Celery
Stacks1.7K
Followers1.6K
Votes280
GitHub Stars27.5K
Forks4.9K
Apache NiFi
Apache NiFi
Stacks393
Followers692
Votes65

Apache NiFi vs Celery: What are the differences?

Developers describe Apache NiFi as "A reliable system to process and distribute data". 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. On the other hand, Celery is detailed as "Distributed task queue". 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.

Apache NiFi and Celery are primarily classified as "Stream Processing" and "Message Queue" tools respectively.

Celery is an open source tool with 12.9K GitHub stars and 3.33K GitHub forks. Here's a link to Celery's open source repository on GitHub.

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Detailed Comparison

Celery
Celery
Apache NiFi
Apache NiFi

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.

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.

-
Web-based user interface; Highly configurable; Data Provenance; Designed for extension; Secure
Statistics
GitHub Stars
27.5K
GitHub Stars
-
GitHub Forks
4.9K
GitHub Forks
-
Stacks
1.7K
Stacks
393
Followers
1.6K
Followers
692
Votes
280
Votes
65
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
  • 17
    Visual Data Flows using Directed Acyclic Graphs (DAGs)
  • 8
    Free (Open Source)
  • 7
    Simple-to-use
  • 5
    Scalable horizontally as well as vertically
  • 5
    Reactive with back-pressure
Cons
  • 2
    Memory-intensive
  • 2
    HA support is not full fledge
  • 1
    Kkk
Integrations
No integrations available
MongoDB
MongoDB
Amazon SNS
Amazon SNS
Amazon S3
Amazon S3
Linux
Linux
Amazon SQS
Amazon SQS
Kafka
Kafka
Apache Hive
Apache Hive
macOS
macOS

What are some alternatives to Celery, Apache NiFi?

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.

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.

Apache Storm

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

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