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  1. Stackups
  2. DevOps
  3. Build Automation
  4. Infrastructure Build Tools
  5. Atlas vs Falcor

Atlas vs Falcor

OverviewComparisonAlternatives

Overview

Atlas
Atlas
Stacks33
Followers125
Votes0
Falcor
Falcor
Stacks27
Followers79
Votes14
GitHub Stars10.6K
Forks449

Atlas vs Falcor: What are the differences?

Introduction:

Key differences between Atlas and Falcor:

1. **Data Fetching Approach**: Atlas follows a traditional data-fetching approach where each component individually requests data from the server. In contrast, Falcor implements a smart caching mechanism that allows components to specify their data needs in a single request to the server, improving performance and reducing network overhead.
   
2. **Data Caching Strategy**: Atlas relies on client-side caching, which requires more manual management to ensure data consistency and avoid duplication. On the other hand, Falcor employs a more sophisticated caching strategy by storing data in a centralized cache that can be shared across multiple components, enabling efficient data retrieval and updates.

3. **Error Handling**: In Atlas, error handling is typically handled at the component level, where developers need to implement error-catching logic within each component that interacts with the server. Falcor, however, provides a centralized error handling mechanism that allows developers to define how errors are handled globally, simplifying the error management process.

4. **Data Synchronization**: Atlas lacks built-in support for data synchronization across multiple clients, making it challenging to maintain data consistency in real-time collaborative applications. Falcor, on the other hand, offers seamless data synchronization capabilities through its centralized caching system, ensuring that all connected clients have access to the most up-to-date data.

5. **Granularity of Data Requests**: Atlas requires components to explicitly specify the data they need, resulting in potential over-fetching or under-fetching of data. Falcor, with its path-based data retrieval approach, allows for more fine-grained control over the data requested, reducing unnecessary data transfer and improving overall performance.

6. **Ease of Integration**: Atlas is more tightly coupled with specific UI frameworks, making integration with different front-end technologies more challenging. Falcor, being agnostic to UI frameworks, provides more flexibility in integrating with various front-end technologies, simplifying the development process for teams using diverse tech stacks.

In Summary, Atlas and Falcor differ in their data-fetching approach, caching strategy, error handling, data synchronization, data request granularity, and ease of integration.

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Detailed Comparison

Atlas
Atlas
Falcor
Falcor

Atlas is one foundation to manage and provide visibility to your servers, containers, VMs, configuration management, service discovery, and additional operations services.

Falcor lets you represent all your remote data sources as a single domain model via a virtual JSON graph. You code the same way no matter where the data is, whether in memory on the client or over the network on the server.

One command to develop any application: vagrant up;One command to deploy any application: vagrant push
One Model Everywhere;The Data is the API;Bind to the Cloud
Statistics
GitHub Stars
-
GitHub Stars
10.6K
GitHub Forks
-
GitHub Forks
449
Stacks
33
Stacks
27
Followers
125
Followers
79
Votes
0
Votes
14
Pros & Cons
No community feedback yet
Pros
  • 2
    Data is the API
  • 2
    One Model Everywhere
  • 2
    Promotes microservices
  • 2
    Small API
  • 1
    Bind to the Cloud

What are some alternatives to Atlas, Falcor?

Postman

Postman

It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide.

Paw

Paw

Paw is a full-featured and beautifully designed Mac app that makes interaction with REST services delightful. Either you are an API maker or consumer, Paw helps you build HTTP requests, inspect the server's response and even generate client code.

AWS CloudFormation

AWS CloudFormation

You can use AWS CloudFormation’s sample templates or create your own templates to describe the AWS resources, and any associated dependencies or runtime parameters, required to run your application. You don’t need to figure out the order in which AWS services need to be provisioned or the subtleties of how to make those dependencies work.

Karate DSL

Karate DSL

Combines API test-automation, mocks and performance-testing into a single, unified framework. The BDD syntax popularized by Cucumber is language-neutral, and easy for even non-programmers. Besides powerful JSON & XML assertions, you can run tests in parallel for speed - which is critical for HTTP API testing.

Appwrite

Appwrite

Appwrite's open-source platform lets you add Auth, DBs, Functions and Storage to your product and build any application at any scale, own your data, and use your preferred coding languages and tools.

Runscope

Runscope

Keep tabs on all aspects of your API's performance with uptime monitoring, integration testing, logging and real-time monitoring.

Insomnia REST Client

Insomnia REST Client

Insomnia is a powerful REST API Client with cookie management, environment variables, code generation, and authentication for Mac, Window, and Linux.

Packer

Packer

Packer automates the creation of any type of machine image. It embraces modern configuration management by encouraging you to use automated scripts to install and configure the software within your Packer-made images.

RAML

RAML

RESTful API Modeling Language (RAML) makes it easy to manage the whole API lifecycle from design to sharing. It's concise - you only write what you need to define - and reusable. It is machine readable API design that is actually human friendly.

Apigee

Apigee

API management, design, analytics, and security are at the heart of modern digital architecture. The Apigee intelligent API platform is a complete solution for moving business to the digital world.

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