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Pusher VS Google Cloud Dataflow

Compare Pusher VS Google Cloud Dataflow and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Pusher logo Pusher

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

Google Cloud Dataflow logo Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
  • Pusher Landing page
    Landing page //
    2023-10-05
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

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.

Google Cloud Dataflow features and specs

  • Scalability
    Google Cloud Dataflow can automatically scale up or down depending on your data processing needs, handling massive datasets with ease.
  • Fully Managed
    Dataflow is a fully managed service, which means you don't have to worry about managing the underlying infrastructure.
  • Unified Programming Model
    It provides a single programming model for both batch and streaming data processing using Apache Beam, simplifying the development process.
  • Integration
    Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Bigtable.
  • Real-time Analytics
    Supports real-time data processing, enabling quicker insights and facilitating faster decision-making.
  • Cost Efficiency
    Pay-as-you-go pricing model ensures you only pay for resources you actually use, which can be cost-effective.
  • Global Availability
    Cloud Dataflow is available globally, which allows for regionalized data processing.
  • Fault Tolerance
    Built-in fault tolerance mechanisms help ensure uninterrupted data processing.

Possible disadvantages of Google Cloud Dataflow

  • Steep Learning Curve
    The complexity of using Apache Beam and understanding its model can be challenging for beginners.
  • Debugging Difficulties
    Debugging data processing pipelines can be complex and time-consuming, especially for large-scale data flows.
  • Cost Management
    While it can be cost-efficient, the costs can rise quickly if not monitored properly, particularly with real-time data processing.
  • Vendor Lock-in
    Using Google Cloud Dataflow can lead to vendor lock-in, making it challenging to migrate to another cloud provider.
  • Limited Support for Non-Google Services
    While it integrates well within Google Cloud, support for non-Google services may not be as robust.
  • Latency
    There can be some latency in data processing, especially when dealing with high volumes of data.
  • Complexity in Pipeline Design
    Designing pipelines to be efficient and cost-effective can be complex, requiring significant expertise.

Pusher videos

+ Add

Mark Kermode reviews Pusher

More videos:

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

Google Cloud Dataflow videos

+ Add

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Category Popularity

0-100% (relative to Pusher and Google Cloud Dataflow)

Pusher

Google Cloud Dataflow

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

User comments

Share your experience with using Pusher and Google Cloud Dataflow. 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 Google Cloud Dataflow

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

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Social recommendations and mentions

Based on our record, Pusher should be more popular than Google Cloud Dataflow. It has been mentiond 55 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 / 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 / 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 / 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

Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
View more

What are some alternatives?

When comparing Pusher and Google Cloud Dataflow, you can also consider the following products

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

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.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

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