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  5. Confluent vs IBM MQ

Confluent vs IBM MQ

OverviewComparisonAlternatives

Overview

Confluent
Confluent
Stacks337
Followers239
Votes14
IBM MQ
IBM MQ
Stacks118
Followers187
Votes11

Confluent vs IBM MQ: What are the differences?

# Introduction
This comparison between Confluent and IBM MQ will help you understand the key differences between the two solutions.

1. **Architecture**: Confluent is built on top of Apache Kafka, a distributed streaming platform, while IBM MQ is a message-oriented middleware product that ensures reliable and secure communication between applications.
2. **Language Support**: Confluent supports multiple programming languages like Java, Python, and Go for developing Kafka-based applications, whereas IBM MQ offers support for a wide range of languages including Java, C, and .NET.
3. **Scalability**: Confluent leverages Kafka's scalable architecture allowing horizontal scaling of clusters, whereas IBM MQ traditionally uses a hub-and-spoke model which may require additional configuration for scaling.
4. **Real-time Data Processing**: Confluent is well-suited for real-time data processing and stream analytics with its integration capabilities with Apache Kafka Streams, while IBM MQ is more focused on reliable messaging and transaction processing.
5. **Open Source vs Commercial**: Confluent is based on open-source Apache Kafka, offering community support and a free version, while IBM MQ is a commercial product with a licensing cost attached for enterprise usage.
6. **Ecosystem Integration**: Confluent provides a full ecosystem of tools such as Confluent Control Center for monitoring, KSQL for stream processing, and Schema Registry for schema management, while IBM MQ offers a range of additional products for service integration, workload balancing, and more.

In Summary, Confluent and IBM MQ differ in their architecture, language support, scalability, focus on real-time data processing, pricing models, and ecosystem integrations.

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Detailed Comparison

Confluent
Confluent
IBM MQ
IBM MQ

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

It is a messaging middleware that simplifies and accelerates the integration of diverse applications and business data across multiple platforms. It offers proven, enterprise-grade messaging capabilities that skillfully and safely move information.

Reliable; High-performance stream data platform; Manage and organize data from different sources.
Once-and-once-only delivery; Asynchronous messaging; Powerful protection; Simplified, smart management; Augmented security; Expanded client application options
Statistics
Stacks
337
Stacks
118
Followers
239
Followers
187
Votes
14
Votes
11
Pros & Cons
Pros
  • 4
    Free for casual use
  • 3
    Dashboard for kafka insight
  • 3
    No hypercloud lock-in
  • 2
    Easily scalable
  • 2
    Zero devops
Cons
  • 1
    Proprietary
Pros
  • 3
    Useful for big enteprises
  • 3
    Reliable for banking transactions
  • 2
    Secure
  • 1
    Broader connectivity - more protocols, APIs, Files etc
  • 1
    High Availability
Cons
  • 2
    Cost
Integrations
Microsoft SharePoint
Microsoft SharePoint
Java
Java
Python
Python
Salesforce Sales Cloud
Salesforce Sales Cloud
Kafka Streams
Kafka Streams
No integrations available

What are some alternatives to Confluent, IBM MQ?

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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