Software Alternatives, Accelerators & Startups

PubNub VS Google Cloud Dataflow

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

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

PubNub logo 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.

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.
  • PubNub Landing page
    Landing page //
    2023-10-05
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

PubNub features and specs

  • Real-time Messaging
    PubNub provides real-time messaging capabilities, allowing for efficient, low-latency communication between clients and servers. This is ideal for applications that require instant data updates, such as chat apps, live dashboards, and multiplayer games.
  • Global Infrastructure
    The service operates on a global network of data centers, ensuring that messages are delivered quickly and reliably across different geographic locations. This reduces latency and enhances the performance and user experience of apps that serve a global user base.
  • Scalability
    PubNub can handle millions of messages and connections simultaneously, making it highly scalable for applications with substantial user bases or high traffic requirements.
  • Security
    PubNub offers a range of security features including TLS encryption, token-based authentication, and access management to ensure that data is securely transmitted and received.
  • Extensive SDKs
    The platform provides extensive SDKs (Software Development Kits) for a wide variety of programming languages and platforms, making it easier for developers to integrate PubNub into their applications.
  • Feature-rich
    Besides messaging, PubNub offers additional features like presence, storage and playback, message history, and mobile push notifications, which add significant value to developers.
  • Reliability
    With multiple layers of failover and redundancy, PubNub is designed to deliver high availability and uptime, which is critical for mission-critical applications.

Possible disadvantages of PubNub

  • Cost
    PubNub can be expensive for large scale applications or startups with limited budgets. The pricing model is based on usage, making it difficult to predict costs for applications that experience rapid growth or variable traffic.
  • Complexity
    The platform offers a rich set of features, which can make it complex for new developers to fully understand and implement. The learning curve can be steep, particularly for those who are unfamiliar with real-time messaging systems.
  • Vendor Lock-In
    Relying heavily on PubNub's infrastructure and APIs means that migrating to another service can be challenging and time-consuming. This can create a dependency that might be problematic if you ever need to switch providers.
  • Latency Variability
    While PubNub generally offers low-latency performance, there can be variability in latency due to network conditions or other factors, which might affect time-sensitive applications.
  • Limited Customization
    Although PubNub offers many features, there can be limitations in terms of customizing certain functionalities or integrating with specific business requirements that are not directly supported by the platform.
  • Documentation
    While PubNub provides comprehensive documentation, some users report that it can be inconsistent or lacking in certain areas, making it difficult to troubleshoot specific issues or fully utilize advanced features without additional support.
  • Dependency on Internet
    As a cloud-based service, PubNub relies on a stable internet connection. Applications that need to function in environments with unreliable or no internet connectivity may face challenges using this service.

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.

PubNub videos

Realtime Bike Tracking App with MediaTek and PubNub

More videos:

  • Review - Streaming Realtime Analytics with Keen IO and PubNub
  • Review - PubNub IOT House Training Video

Google Cloud Dataflow videos

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 PubNub and Google Cloud Dataflow)
Developer Tools
100 100%
0% 0
Big Data
0 0%
100% 100
Mobile Push Messaging
100 100%
0% 0
Data Dashboard
0 0%
100% 100

User comments

Share your experience with using PubNub 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 PubNub and Google Cloud Dataflow

PubNub Reviews

2023 Firebase Alternatives: Top 10 Open-Source & Free
PubNub is another new but advanced Firebase alternative in our list. It handles 3 trillion monthly calls with 99.999% uptime and accesses 800 million devices over the globe. The renowned customers of PubNub are Kustomer, vFairs and Swiggy.
SignalR Alternatives
Pubnub is considered one of the good alternatives to SignalR because it comes to the rescue when secured data streams get exchanged over the network with API that helps in connecting with the applications to the real-time one.
Source: www.educba.com
Firebase Alternatives – Top 10 Competitors
Pubnub IoT Device Control Real-time updates Storage & Playback Stream Controller Push Notifications Analytics Access Management
Top 10 Alternatives To Firebase
PubNub is popular for its versatile streaming abilities. You can create promising web and mobile apps with PubNub.
Source: www.redbytes.in

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, Google Cloud Dataflow seems to be a lot more popular than PubNub. While we know about 14 links to Google Cloud Dataflow, we've tracked only 1 mention of PubNub. 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.

PubNub mentions (1)

  • gRPC test-and-try with Akka Serverless and Evans
    I first learned about gRPC about five years go. Since that moment in time when an engineer at PubNub introduced me to the framework, I have let the idea of gRPC simmer more in the background, especially since I was already rather steeped in REST, Open API, Swagger, and other sundries seen in the broader API space (miss you, Mashery. There seemed to be plenty of great tooling, documentation and technologies... - Source: dev.to / over 3 years ago

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
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What are some alternatives?

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

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

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

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

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

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

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