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  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Confluent vs IronMQ

Confluent vs IronMQ

OverviewComparisonAlternatives

Overview

IronMQ
IronMQ
Stacks35
Followers49
Votes36
Confluent
Confluent
Stacks337
Followers239
Votes14

IronMQ vs Confluent: What are the differences?

Developers describe IronMQ as "Message Queue for any deployment". 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. On the other hand, Confluent is detailed as "We make a stream data platform to help companies harness their high volume real-time data streams". 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.

IronMQ belongs to "Message Queue" category of the tech stack, while Confluent can be primarily classified under "Stream Processing".

Some of the features offered by IronMQ are:

  • Instant High Availability- Runs on top cloud infrastructures and uses multiple high-availability data centers. Uses reliable datastores for message durability and persistence.
  • Easy to Use- IronMQ is super easy to use. Simply connect directly to the API endpoints and you're ready to create and use queues. There are also client libraries available in any language you want – Ruby, Python, PHP, Java, .NET, Go, Node.JS, and more
  • Scalable / High Performance- Built using high-performance languages designed for concurrency and runs on industrial-strength clouds. Push messages and stream data at will without worrying about memory limits or adding more servers.

On the other hand, Confluent provides the following key features:

  • Reliable
  • High-performance stream data platform
  • Manage and organize data from different sources.

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Detailed Comparison

IronMQ
IronMQ
Confluent
Confluent

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.

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

Instant High Availability- Runs on top cloud infrastructures and uses multiple high-availability data centers. Uses reliable datastores for message durability and persistence.;Easy to Use- IronMQ is super easy to use. Simply connect directly to the API endpoints and you're ready to create and use queues. There are also client libraries available in any language you want – Ruby, Python, PHP, Java, .NET, Go, Node.JS, and more;Scalable / High Performance- Built using high-performance languages designed for concurrency and runs on industrial-strength clouds. Push messages and stream data at will without worrying about memory limits or adding more servers.;Realtime Monitoring- Get realtime monitoring of your message queues through IronMQ's beautiful dashboard. This allows you to quickly find, diagnose, and resolve problems before others notice.;One-time FIFO delivery;Push Queues and publish-subscribe support;Queue messages using webhooks
Reliable; High-performance stream data platform; Manage and organize data from different sources.
Statistics
Stacks
35
Stacks
337
Followers
49
Followers
239
Votes
36
Votes
14
Pros & Cons
Pros
  • 12
    Great Support
  • 8
    Heroku Add-on
  • 3
    Push support
  • 3
    Delayed delivery upto 7 days
  • 2
    GDPR Compliant
Cons
  • 1
    Can't use rabbitmqadmin
Pros
  • 4
    Free for casual use
  • 3
    No hypercloud lock-in
  • 3
    Dashboard for kafka insight
  • 2
    Zero devops
  • 2
    Easily scalable
Cons
  • 1
    Proprietary
Integrations
Amazon EC2
Amazon EC2
Heroku
Heroku
Engine Yard Cloud
Engine Yard Cloud
Rackspace Cloud Servers
Rackspace Cloud Servers
Red Hat OpenShift
Red Hat OpenShift
StackMob
StackMob
AppFog
AppFog
cloudControl
cloudControl
Microsoft SharePoint
Microsoft SharePoint
Java
Java
Python
Python
Salesforce Sales Cloud
Salesforce Sales Cloud
Kafka Streams
Kafka Streams

What are some alternatives to IronMQ, 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.

Celery

Celery

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.

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.

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