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Pusher VS Apache Spark

Compare Pusher VS Apache Spark and see what are their differences

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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 Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Pusher Landing page
    Landing page //
    2023-10-05
  • Apache Spark Landing page
    Landing page //
    2021-12-31

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 Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Analysis of Pusher

Overall verdict

  • Pusher is a solid choice for real-time communication needs. Its ease of integration, wide range of supported platforms, and reliable performance make it a good fit for many applications. However, like any third-party service, it's important to consider factors such as cost, specific requirements, and potential vendor lock-in.

Why this product is good

  • Pusher is a popular service for adding real-time functionality to applications. It offers robust APIs that make it easier to implement features such as notifications, live updates, and real-time chat functionality. Pusher's service is known for its reliability, ease of use, and scalability, making it a strong choice for developers who need to integrate real-time capabilities without building infrastructure from scratch.

Recommended for

  • Developers building real-time chat applications
  • Teams needing live updating features
  • Projects requiring scalable and reliable real-time communication
  • Developers looking for a quick-to-implement solution for notifications and updates

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Pusher videos

+ Add

Mark Kermode reviews Pusher

More videos:

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

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

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

Pusher

Apache Spark

Mobile Push Messaging
100 100%
Mobile Push Messaging
0% 0
Databases
0 0%
Databases
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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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 Spark

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 Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Apache Spark might be a bit more popular than Pusher. We know about 70 links to it since March 2021 and only 55 links to Pusher. 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 / 3 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 / 9 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 / 9 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 / 9 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 / 10 months ago
View more

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 2 months ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 2 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 3 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 4 months ago
View more

What are some alternatives?

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

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

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

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

Hadoop - Open-source software for reliable, scalable, distributed computing

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.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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