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  4. Stream Processing
  5. Apache Storm vs Confluent

Apache Storm vs Confluent

OverviewComparisonAlternatives

Overview

Apache Storm
Apache Storm
Stacks207
Followers282
Votes25
GitHub Stars6.7K
Forks4.1K
Confluent
Confluent
Stacks337
Followers239
Votes14

Apache Storm vs Confluent: What are the differences?

Apache Storm vs. Confluent

1. **Real-time data processing**: Apache Storm is a real-time computation system that allows processing streaming data as it arrives. On the other hand, Confluent is a platform that provides tools and services for stream processing, built on top of Apache Kafka. While Storm is more focused on real-time data processing, Confluent offers a broader range of capabilities.
2. **Scalability**: Apache Storm is highly scalable and can handle large volumes of data across distributed systems, making it suitable for high-throughput applications. Confluent, with its integration with Apache Kafka, also offers scalability but focuses more on providing a unified platform for stream processing applications.
3. **Ease of Use**: Confluent provides a more user-friendly and streamlined experience for setting up and managing stream processing pipelines compared to Apache Storm. This makes it easier for developers and organizations to adopt and integrate streaming technologies into their workflows.
4. **Ecosystem Integration**: Apache Storm has a mature ecosystem with a wide range of integrations and support for various programming languages. In contrast, Confluent comes with a built-in ecosystem that integrates seamlessly with Apache Kafka, allowing users to leverage the existing Kafka ecosystem for stream processing.
5. **Monitoring and Management**: Confluent provides enhanced monitoring and management features compared to Apache Storm. With Confluent Control Center, users have better visibility into their stream processing applications, enabling them to monitor performance, troubleshoot issues, and optimize their workflows more effectively.
6. **Cost and Licensing**: Apache Storm is open-source software and is free to use, while Confluent offers both open-source components and commercial services. Depending on the requirements and resources available, organizations can choose between the cost-effective option of Apache Storm or the added features and support provided by Confluent.

In Summary, the key differences between Apache Storm and Confluent lie in real-time data processing focus, scalability, ease of use, ecosystem integration, monitoring and management capabilities, and cost and licensing models.

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Detailed Comparison

Apache Storm
Apache Storm
Confluent
Confluent

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.

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

Storm integrates with the queueing and database technologies you already use;Simple API;Scalable;Fault tolerant;Guarantees data processing;Use with any language;Easy to deploy and operate;Free and open source
Reliable; High-performance stream data platform; Manage and organize data from different sources.
Statistics
GitHub Stars
6.7K
GitHub Stars
-
GitHub Forks
4.1K
GitHub Forks
-
Stacks
207
Stacks
337
Followers
282
Followers
239
Votes
25
Votes
14
Pros & Cons
Pros
  • 10
    Flexible
  • 6
    Easy setup
  • 4
    Event Processing
  • 3
    Clojure
  • 2
    Real Time
Pros
  • 4
    Free for casual use
  • 3
    No hypercloud lock-in
  • 3
    Dashboard for kafka insight
  • 2
    Easily scalable
  • 2
    Zero devops
Cons
  • 1
    Proprietary
Integrations
No integrations available
Microsoft SharePoint
Microsoft SharePoint
Java
Java
Python
Python
Salesforce Sales Cloud
Salesforce Sales Cloud
Kafka Streams
Kafka Streams

What are some alternatives to Apache Storm, 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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