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Confluent

232
231
+ 1
14
MQTT

592
570
+ 1
7
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MQTT vs Confluent: What are the differences?

MQTT: A machine-to-machine Internet of Things connectivity protocol. It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium; Confluent: 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.

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

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Pros of Confluent
Pros of MQTT
  • 4
    Free for casual use
  • 3
    No hypercloud lock-in
  • 3
    Dashboard for kafka insight
  • 2
    Easily scalable
  • 2
    Zero devops
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Lightweight with a relatively small data footprint
  • 2
    Very easy to configure and use with open source tools

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Cons of Confluent
Cons of MQTT
  • 1
    Proprietary
  • 1
    Easy to configure in an unsecure manner

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What is Confluent?

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

What is MQTT?

It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium.

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What companies use Confluent?
What companies use MQTT?
See which teams inside your own company are using Confluent or MQTT.
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What tools integrate with Confluent?
What tools integrate with MQTT?

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What are some alternatives to Confluent and MQTT?
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
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 gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
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.
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.
See all alternatives