Apache Pulsar vs Confluent

Overview

Apache Pulsar

Stacks118

Followers199

Votes24

Confluent

Stacks337

Followers239

Votes14

Apache Pulsar vs Confluent: What are the differences?

Apache Pulsar and Confluent both play crucial roles in stream processing and messaging, but they have key differences that set them apart.

Architecture: Apache Pulsar is designed as a multi-tenant, high-performance solution with a separation of compute and storage layers, enabling scalability and flexibility. On the other hand, Confluent is built as an event streaming platform based on Apache Kafka, emphasizing the use of Kafka Connect and Kafka Streams for stream processing.
Messaging Model: Apache Pulsar supports both traditional queuing and publish-subscribe messaging paradigms, providing more flexibility for different use cases. In contrast, Confluent focuses primarily on the publish-subscribe model, with Kafka's topic-based messaging system at its core.
Integration: Apache Pulsar is more interoperable with external systems and tools, offering robust connectors for seamless integration with various databases and services. Confluent, being tightly integrated with Kafka, excels in ecosystem compatibility and seamless interactions within the Kafka ecosystem.
Ease of Use: Confluent provides extensive tooling and support for managing Kafka clusters and stream processing applications, making it easier for users to set up and maintain their data pipelines. In comparison, while Apache Pulsar offers similar functionalities, it may require more configuration and management effort due to its distributed nature.
Scalability: Apache Pulsar's architecture enables easy horizontal scalability by adding more instances to the compute and storage layers independently, providing better resource utilization and performance optimization. Confluent also offers scalability options, but its design is more closely tied to Kafka's distributed architecture, which may require additional considerations for scaling.

In Summary, Apache Pulsar and Confluent differ in their architecture, messaging model, integration capabilities, ease of use, and scalability options.

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Detailed Comparison

Apache Pulsar	Confluent
Apache Pulsar is a distributed messaging solution developed and released to open source at Yahoo. Pulsar supports both pub-sub messaging and queuing in a platform designed for performance, scalability, and ease of development and operation.	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
Unified model supporting pub-sub messaging and queuing; Easy scalability to millions of topics; Native multi-datacenter replication; Multi-language client API; Guaranteed data durability; Scalable distributed storage leveraging Apache BookKeeper	Reliable; High-performance stream data platform; Manage and organize data from different sources.
Statistics
Stacks 118	Stacks 337
Followers 199	Followers 239
Votes 24	Votes 14
Pros & Cons
Pros 7 Simple 4 Scalable 3 High-throughput 2 Geo-replication 2 Multi-tenancy Cons 1 LImited Language support(6) 1 Very few commercial vendors for support 1 Only Supports Topics 1 Not jms compliant 1 No guaranteed dliefvery	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 Java Python Salesforce Sales Cloud Kafka Streams

What are some alternatives to Apache Pulsar, Confluent?

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.

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

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 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

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

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

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 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

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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Apache Pulsar vs Confluent: What are the differences?

Apache Pulsar and Confluent both play crucial roles in stream processing and messaging, but they have key differences that set them apart.

Architecture: Apache Pulsar is designed as a multi-tenant, high-performance solution with a separation of compute and storage layers, enabling scalability and flexibility. On the other hand, Confluent is built as an event streaming platform based on Apache Kafka, emphasizing the use of Kafka Connect and Kafka Streams for stream processing.
Messaging Model: Apache Pulsar supports both traditional queuing and publish-subscribe messaging paradigms, providing more flexibility for different use cases. In contrast, Confluent focuses primarily on the publish-subscribe model, with Kafka's topic-based messaging system at its core.
Integration: Apache Pulsar is more interoperable with external systems and tools, offering robust connectors for seamless integration with various databases and services. Confluent, being tightly integrated with Kafka, excels in ecosystem compatibility and seamless interactions within the Kafka ecosystem.
Ease of Use: Confluent provides extensive tooling and support for managing Kafka clusters and stream processing applications, making it easier for users to set up and maintain their data pipelines. In comparison, while Apache Pulsar offers similar functionalities, it may require more configuration and management effort due to its distributed nature.
Scalability: Apache Pulsar's architecture enables easy horizontal scalability by adding more instances to the compute and storage layers independently, providing better resource utilization and performance optimization. Confluent also offers scalability options, but its design is more closely tied to Kafka's distributed architecture, which may require additional considerations for scaling.

In Summary, Apache Pulsar and Confluent differ in their architecture, messaging model, integration capabilities, ease of use, and scalability options.