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Google App Engine VS Apache Storm

Compare Google App Engine VS Apache Storm and see what are their differences

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Contents:
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  3. » Reviews
  4. » Alternatives

Google App Engine logo Google App Engine

A powerful platform to build web and mobile apps that scale automatically.

Apache Storm logo Apache Storm

Apache Storm is a free and open source distributed realtime computation system.
  • Google App Engine Landing page
    Landing page //
    2023-10-17
  • Apache Storm Landing page
    Landing page //
    2019-03-11

Google App Engine features and specs

  • Auto-scaling
    Google App Engine automatically scales your application based on the traffic it receives, ensuring that your application can handle varying workloads without manual intervention.
  • Managed environment
    App Engine provides a fully managed environment, covering infrastructure management tasks like server provisioning, patching, monitoring, and managing app versions.
  • Integrated services
    Seamlessly integrates with other Google Cloud services such as Datastore, Cloud SQL, Pub/Sub, and more, offering a comprehensive ecosystem for building and deploying applications.
  • Multiple languages support
    Supports multiple programming languages including Java, Python, PHP, Node.js, Go, Ruby, and .NET, giving developers flexibility in choosing their preferred language.
  • Security
    Offers robust security features including Identity and Access Management (IAM), Cloud Identity, and automated security updates, which help protect your applications from vulnerabilities.
  • Developer productivity
    App Engine allows rapid development and deployment, letting developers focus on writing code without worrying about infrastructure management, thus boosting productivity.
  • Versioning
    Supports versioning of applications, allowing multiple versions of the application to be hosted simultaneously, which helps in A/B testing and rollback capabilities.

Possible disadvantages of Google App Engine

  • Cost
    While you pay for what you use, costs can escalate quickly with high traffic or resource-intensive applications. Detailed cost prediction can be challenging.
  • Vendor lock-in
    Relying heavily on Google App Engine's proprietary services and APIs can make it difficult to migrate applications to other platforms, leading to vendor lock-in.
  • Limited control
    Being a fully managed service, App Engine provides limited control over the underlying infrastructure which might be a limitation for certain advanced use cases.
  • Environment constraints
    Certain restrictions and limitations are imposed on the runtime environment, such as request timeout limits and specific resource quotas, which can affect application performance.
  • Complex debugging
    Debugging issues in a highly abstracted managed environment can be more complex and difficult compared to traditional server-hosted applications.
  • Cold start latency
    Serverless environments like App Engine can suffer from cold start latency, where the initial request triggers a delay as the environment spins up resources.
  • Configuration complexity
    Despite its benefits, configuring and optimizing App Engine for specific scenarios can be more complex than expected, requiring a steep learning curve.

Apache Storm features and specs

  • Real-Time Processing
    Apache Storm is designed for processing data in real-time, which makes it ideal for applications like fraud detection, recommendation systems, and monitoring tools.
  • Scalability
    Storm is capable of scaling horizontally, allowing it to handle increasing amounts of data by adding more nodes, making it suitable for large-scale applications.
  • Fault Tolerance
    Storm provides robust fault-tolerance mechanisms by rerouting tasks from failed nodes to operational ones, ensuring continuous processing.
  • Broad Language Support
    Apache Storm supports multiple programming languages, including Java, Python, and Ruby, allowing developers to use the language they are most comfortable with.
  • Open Source Community
    Being an Apache project, Storm benefits from a strong open-source community, which contributes to its development and offers abundant resources and support.

Possible disadvantages of Apache Storm

  • Complex Setup
    Setting up and configuring Apache Storm can be complex and time-consuming, requiring detailed knowledge of its architecture and the underlying infrastructure.
  • High Learning Curve
    The architecture and components of Storm can be difficult for new users to grasp, leading to a steeper learning curve compared to some other streaming platforms.
  • Maintenance Overhead
    Managing and maintaining a Storm cluster can require significant effort, including monitoring, troubleshooting, and scaling the infrastructure.
  • Error Handling
    While Storm is fault-tolerant, its error handling at the application level can sometimes be challenging, requiring careful design to manage failures effectively.
  • Resource Intensive
    Storm can be resource-intensive, particularly in terms of memory and CPU usage, which can lead to increased costs and necessitate powerful hardware.

Analysis of Google App Engine

Overall verdict

  • Google App Engine is generally considered a good choice for developers looking for a serverless platform to deploy their applications quickly without managing underlying infrastructure. Its ease of use, scalability, and integration with Google's ecosystem make it a strong option, especially for projects expecting to scale significantly or require integration with other Google Cloud services.

Why this product is good

  • Google App Engine is a fully managed serverless platform that allows developers to build scalable web applications and mobile backends. It abstracts away infrastructure management, handles scaling automatically, and offers integration with other Google Cloud services, providing a high degree of flexibility and efficiency. Its key strengths include support for multiple programming languages, built-in security features, and seamless connectivity to Google's machine learning and data analytics tools.

Recommended for

    Google App Engine is recommended for developers building web applications who prefer a Platform as a Service (PaaS) model, startups who need a solution that can grow with them without worrying about scaling issues, teams wanting to leverage Google's robust data and analytics offerings, and businesses that require a global reach with reliable performance.

Google App Engine videos

+ Add

Get to know Google App Engine

More videos:

  • Review - Developing apps that scale automatically with Google App Engine

Apache Storm videos

+ Add

Apache Storm Tutorial For Beginners | Apache Storm Training | Apache Storm Example | Edureka

More videos:

  • Review - Developing Java Streaming Applications with Apache Storm
  • Review - Atom Text Editor Option - Real-Time Analytics with Apache Storm

Category Popularity

0-100% (relative to Google App Engine and Apache Storm)

Google App Engine

Apache Storm

Cloud Computing
100 100%
Cloud Computing
0% 0
Big Data
0 0%
Big Data
100% 100
Cloud Hosting
100 100%
Cloud Hosting
0% 0
Stream Processing
0 0%
Stream Processing
100% 100

User comments

Share your experience with using Google App Engine and Apache Storm. 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 Google App Engine and Apache Storm

Google App Engine Reviews

Top 5 Alternatives to Heroku
Google App Engine is fast, easy, but not that very cheap. The pricing is reasonable, and it comes with a free tier, which is great for small projects that are right for beginner developers who want to quickly set up their apps. It can also auto scale, create new instances as needed and automatically handle high availability. App Engine gets a positive rating for performance...
Source: blog.back4app.com
AppScale - The Google App Engine Alternative
AppScale is open source Google App Engine and allows you to run your GAE applications on any infrastructure, anywhere that makes sense for your business. AppScale eliminates lock-in and makes your GAE application portable. This way you can choose which public or private cloud platform is the best fit for your business requirements. Because we are literally the GAE...
Source: blog.appscale.com

Apache Storm Reviews

Top 15 Kafka Alternatives Popular In 2021
Apache Storm is a recognized, distributed, open-source real-time computational system. It is free, simple to use, and helps in easily and accurately processing multiple data streams in real-time. Because of its simplicity, it can be utilized with any programming language and that is one reason it is a developer’s preferred choice. It is fast, scalable, and integrates well...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Storm is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Built by Twitter, Apache Storm specifically aims at the transformation of data streams. Storm has many use cases like real-time analytics, online machine...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Google App Engine should be more popular than Apache Storm. It has been mentiond 31 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.

Google App Engine mentions (31)

  • Guide to modern app-hosting without servers on Google Cloud
    If Google App Engine (GAE) is the "OG" serverless platform, Cloud Run (GCR) is its logical successor, crafted for today's modern app-hosting needs. GAE was the 1st generation of Google serverless platforms. It has since been joined, about a decade later, by 2nd generation services, GCR and Cloud Functions (GCF). GCF is somewhat out-of-scope for this post so I'll cover that another time. - Source: dev.to / 5 months ago
  • Security in the Cloud: Your Role in the Shared Responsibility Model
    As Windsales Inc. expands, it adopts a PaaS model to offload server and runtime management, allowing its developers and engineers to focus on code development and deployment. By partnering with providers like Heroku and Google App Engine, Windsales Inc. Accesses a fully managed runtime environment. This choice relieves Windsales Inc. Of managing servers, OS updates, or runtime environment behavior. Instead,... - Source: dev.to / 7 months ago
  • Hosting apps in the cloud with Google App Engine in 2024
    Google App Engine (GAE) is their original serverless solution and first cloud product, launching in 2008 (video), giving rise to Serverless 1.0 and the cloud computing platform-as-a-service (PaaS) service level. It didn't do function-hosting nor was the concept of containers mainstream yet. GAE was specifically for (web) app-hosting (but also supported mobile backends as well). - Source: dev.to / 7 months ago
  • Fixing A Broken Deployment to Google App Engine
    In 2014, I took a web development on Udacity that was taught by Steve Huffman of Reddit fame. He taught authentication, salting passwords, the difference between GET and POST requests, basic html and css, caching techniques. It was a fantastic introduction to web dev. To pass the course, students deployed simple python servers to Google App Engine. When I started to look for work, I opted to use code from that... - Source: dev.to / 11 months ago
  • Next.js Deployment: Vercel's Charm vs. GCP's Muscle
    GCP offers a comprehensive suite of cloud services, including Compute Engine, App Engine, and Cloud Run. This translates to unparalleled control over your infrastructure and deployment configurations. Designed for large-scale applications, GCP effortlessly scales to accommodate significant traffic growth. Additionally, for projects heavily reliant on Google services like BigQuery, Cloud Storage, or AI/ML tools,... - Source: dev.to / 11 months ago
View more

Apache Storm mentions (11)

  • Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges
    There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 2 years ago
  • Real Time Data Infra Stack
    Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 2 years ago
  • In One Minute : Hadoop
    Storm, a system for real-time and stream processing. - Source: dev.to / over 2 years ago
  • Elon Musk reportedly wants to fire 75% of Twitter’s employees
    Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 2 years ago
  • Spark for beginners - and you
    Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago
View more

What are some alternatives?

When comparing Google App Engine and Apache Storm, you can also consider the following products

Salesforce Platform - Salesforce Platform is a comprehensive PaaS solution that paves the way for the developers to test, build, and mitigate the issues in the cloud application before the final deployment.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Dokku - Docker powered mini-Heroku in around 100 lines of Bash

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

Heroku - Agile deployment platform for Ruby, Node.js, Clojure, Java, Python, and Scala. Setup takes only minutes and deploys are instant through git. Leave tedious server maintenance to Heroku and focus on your code.

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

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