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Pusher VS Apache Kafka

Compare Pusher VS Apache Kafka and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
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Pusher logo Pusher

Pusher is a hosted API for quickly, easily and securely adding scalable realtime functionality via WebSockets to web and mobile apps.

Apache Kafka logo Apache Kafka

Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
  • Pusher Landing page
    Landing page //
    2023-10-05
  • Apache Kafka Landing page
    Landing page //
    2022-10-01

Pusher features and specs

  • Real-Time Capabilities
    Pusher offers real-time data transfer, enabling instant updates and live feeds without the need for page refreshes. Its WebSockets-based architecture ensures low latency communication.
  • Ease of Use
    The API is straightforward to integrate, with comprehensive documentation and SDKs for various programming languages and platforms, making implementation quick and painless.
  • Scalability
    Pusher can handle a large number of concurrent connections, making it suitable for applications that need to scale seamlessly as user demand grows.
  • Security
    Pusher provides built-in authentication and authorization options, ensuring that data is secure and accessible only to authorized users.
  • Managed Service
    As a managed service, it eliminates the need for maintaining the infrastructure for real-time functionality, freeing up resources and reducing operational complexity.

Possible disadvantages of Pusher

  • Cost
    Pusher can become expensive, especially for applications with high traffic or requiring a large number of concurrent connections, making it less suitable for startups or small-scale projects on a tight budget.
  • Vendor Lock-In
    Relying heavily on Pusher's services can lead to vendor lock-in, making it challenging to migrate to another service or in-house solution in the future.
  • Limited Offline Functionality
    Pusher is designed for real-time online communication, and it does not inherently support offline capabilities, which can be a limitation for applications that need to function without a constant internet connection.
  • Complexity for Advanced Use Cases
    While it's easy to set up for basic use cases, implementing more complex scenarios may require additional configuration and a deeper understanding of the architecture.
  • Latency
    Even though Pusher boasts low-latency communication, network conditions and geographical distances can still introduce lag, which might not be acceptable for ultra-low-latency requirements like high-frequency trading.

Apache Kafka features and specs

  • High Throughput
    Kafka is capable of handling thousands of messages per second due to its distributed architecture, making it suitable for applications that require high throughput.
  • Scalability
    Kafka can easily scale horizontally by adding more brokers to a cluster, making it highly scalable to serve increased loads.
  • Fault Tolerance
    Kafka has built-in replication, ensuring that data is replicated across multiple brokers, providing fault tolerance and high availability.
  • Durability
    Kafka ensures data durability by writing data to disk, which can be replicated to other nodes, ensuring data is not lost even if a broker fails.
  • Real-time Processing
    Kafka supports real-time data streaming, enabling applications to process and react to data as it arrives.
  • Decoupling of Systems
    Kafka acts as a buffer and decouples the production and consumption of messages, allowing independent scaling and management of producers and consumers.
  • Wide Ecosystem
    The Kafka ecosystem includes various tools and connectors such as Kafka Streams, Kafka Connect, and KSQL, which enrich the functionality of Kafka.
  • Strong Community Support
    Kafka has strong community support and extensive documentation, making it easier for developers to find help and resources.

Possible disadvantages of Apache Kafka

  • Complex Setup and Management
    Kafka's distributed nature can make initial setup and ongoing management complex, requiring expert knowledge and significant administrative effort.
  • Operational Overhead
    Running Kafka clusters involves additional operational overhead, including hardware provisioning, monitoring, tuning, and scaling.
  • Latency Sensitivity
    Despite its high throughput, Kafka may experience increased latency in certain scenarios, especially when configured for high durability and consistency.
  • Learning Curve
    The concepts and architecture of Kafka can be difficult for new users to grasp, leading to a steep learning curve.
  • Hardware Intensive
    Kafka's performance characteristics often require dedicated and powerful hardware, which can be costly to procure and maintain.
  • Dependency Management
    Managing Kafka's dependencies and ensuring compatibility between versions of Kafka, Zookeeper, and other ecosystem tools can be challenging.
  • Limited Support for Small Messages
    Kafka is optimized for large throughput and can be inefficient for applications that require handling a lot of small messages, where overhead can become significant.
  • Operational Complexity for Small Teams
    Smaller teams might find the operational complexity and maintenance burden of Kafka difficult to manage without a dedicated operations or DevOps team.

Pusher videos

+ Add

Mark Kermode reviews Pusher

More videos:

  • Review - Pusher (1996) - Movie Review
  • Review - Film Recommendations: The Pusher Trilogy

Apache Kafka videos

+ Add

Apache Kafka Tutorial | What is Apache Kafka? | Kafka Tutorial for Beginners | Edureka

More videos:

  • Review - Apache Kafka - Getting Started - Kafka Multi-node Cluster - Review Properties
  • Review - 4. Apache Kafka Fundamentals | Confluent Fundamentals for Apache Kafka®
  • Review - Apache Kafka in 6 minutes
  • Review - Apache Kafka Explained (Comprehensive Overview)
  • Review - 2. Motivations and Customer Use Cases | Apache Kafka Fundamentals

Category Popularity

0-100% (relative to Pusher and Apache Kafka)

Pusher

Apache Kafka

Mobile Push Messaging
100 100%
Mobile Push Messaging
0% 0
Stream Processing
0 0%
Stream Processing
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Data Integration
0 0%
Data Integration
100% 100

User comments

Share your experience with using Pusher and Apache Kafka. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Pusher and Apache Kafka

Pusher Reviews

SignalR Alternatives
Pusher as a signal Alternative comes into the picture when it is simple and has free plans for the fallback of SSE to make the frame and log polling also available to the developers for troubleshooting as well.
Source: www.educba.com

Apache Kafka Reviews

Best ETL Tools: A Curated List
Debezium is an open-source Change Data Capture (CDC) tool that originated from RedHat. It leverages Apache Kafka and Kafka Connect to enable real-time data replication from databases. Debezium was partly inspired by Martin Kleppmann’s "Turning the Database Inside Out" concept, which emphasized the power of the CDC for modern data pipelines.
Source: estuary.dev
Best message queue for cloud-native apps
If you take the time to sort out the history of message queues, you will find a very interesting phenomenon. Most of the currently popular message queues were born around 2010. For example, Apache Kafka was born at LinkedIn in 2010, Derek Collison developed Nats in 2010, and Apache Pulsar was born at Yahoo in 2012. What is the reason for this?
Source: docs.vanus.ai
Are Free, Open-Source Message Queues Right For You?
Apache Kafka is a highly scalable and robust messaging queue system designed by LinkedIn and donated to the Apache Software Foundation. It's ideal for real-time data streaming and processing, providing high throughput for publishing and subscribing to records or messages. Kafka is typically used in scenarios that require real-time analytics and monitoring, IoT applications,...
Source: blog.iron.io
10 Best Open Source ETL Tools for Data Integration
It is difficult to anticipate the exact demand for open-source tools in 2023 because it depends on various factors and emerging trends. However, open-source solutions such as Kubernetes for container orchestration, TensorFlow for machine learning, Apache Kafka for real-time data streaming, and Prometheus for monitoring and observability are expected to grow in prominence in...
Source: testsigma.com
11 Best FREE Open-Source ETL Tools in 2024
Apache Kafka is an Open-Source Data Streaming Tool written in Scala and Java. It publishes and subscribes to a stream of records in a fault-tolerant manner and provides a unified, high-throughput, and low-latency platform to manage data.
Source: hevodata.com

Social recommendations and mentions

Based on our record, Apache Kafka should be more popular than Pusher. It has been mentiond 142 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Pusher mentions (55)

  • 5 Must-Watch Tutorials to Build Your SaaS App in 2025
    In this tutorial, you’ll create a Next.js project with TailwindCSS and build custom authentication pages for Clerk without the watermark. This means you’ll create a custom Clerk authentication component, allowing you to have a UI without the Clerk branding in the authentication component. You’ll then set up file uploads using Uploadcare and create custom theming with Shadcn UI for light and dark modes. A real-time... - Source: dev.to / 2 months ago
  • PubNub vs Pusher creating a realtime messaging app in React
    When talking about general IM applications, having the ability to speak to someone in real-time opens up the door to so many unique possibilities. Our world has become ever more connected as a result of these newfound capabilities. In todays article we will learn all about messaging as we build a real-time messaging application. The application will be able to connect to two different real-time application... - Source: dev.to / 8 months ago
  • 10 Must-Use APIs for Your Next SaaS Project
    For real-time notifications, Pusher’s APIs allow you to implement in-app notifications, chat features, and collaboration tools easily. You can find it here. - Source: dev.to / 8 months ago
  • How to Build a Real-time Chat App with Laravel, Vue.js, and Pusher
    Pusher is a cloud-hosted service that makes adding real-time functionality to applications easy. It acts as a real-time communication layer between servers and clients. This allows your backend server to instantly broadcast new data via Pusher to your Vue.js client. - Source: dev.to / 8 months ago
  • Show HN: Webhooked.email (2023)
    Feature request received! Pusher as in this thing -- https://pusher.com/ right? Any other places you want to push to? Slack? - Source: Hacker News / 9 months ago
View more

Apache Kafka mentions (142)

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What are some alternatives?

When comparing Pusher and Apache Kafka, you can also consider the following products

Socket.io - Realtime application framework (Node.JS server)

RabbitMQ - RabbitMQ is an open source message broker software.

Firebase - Firebase is a cloud service designed to power real-time, collaborative applications for mobile and web.

Apache ActiveMQ - Apache ActiveMQ is an open source messaging and integration patterns server.

PubNub - PubNub is a real-time messaging system for web and mobile apps that can handle API for all platforms and push messages to any device anywhere in the world in a fraction of a second without having to worry about proxies, firewalls or mobile drop-offs.

StatCounter - StatCounter is a simple but powerful real-time web analytics service that helps you track, analyse and understand your visitors so you can make good decisions to become more successful online.

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