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JSON Server VS Google Cloud Dataflow

Compare JSON Server VS Google Cloud Dataflow and see what are their differences

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JSON Server logo JSON Server

Get a full fake REST API with zero coding in less than 30 seconds. For front-end developers who need a quick back-end for prototyping and mocking

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.
  • JSON Server Landing page
    Landing page //
    2023-08-01
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

JSON Server features and specs

  • Ease of Setup
    JSON Server can be set up quickly with minimal configuration, making it ideal for prototyping and rapid development. It allows developers to have a fully functioning REST API within minutes.
  • RESTful API
    It provides a standard RESTful API out of the box, allowing developers to perform all CRUD operations. This is helpful for simulating a real-world server while testing client-side applications.
  • Customization
    JSON Server supports middlewares, routes, and custom rules, allowing developers to customize the behavior and structure of the API to better suit their needs.
  • Fakes Backend Data
    It's great for simulating backend responses without needing a real backend setup, useful in front-end development to test components and interactions.
  • Lightweight
    As a lightweight server, it requires fewer resources and is quite simple compared to setting up a full-fledged backend server.

Possible disadvantages of JSON Server

  • Not for Production
    JSON Server is designed for development and testing. It is not suitable for production use due to performance limits and lack of robust security features.
  • Limited Functionality
    While JSON Server is great for basic CRUD operations, it lacks advanced features like authentication, authorization, and complex querying.
  • Data Persistence
    Data is stored in a JSON file, and while this is convenient for testing, it is not suitable for applications that require persistent and scalable data storage.
  • In-memory Limitations
    Being an in-memory server, it may have issues with handling large datasets or complex data structures efficiently.
  • Manual Data Reset
    Any changes made to the JSON file while the server is running require manual resets or reloads to reflect in the API, which can be cumbersome during continuous development cycles.

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.

Analysis of Google Cloud Dataflow

Overall verdict

  • Google Cloud Dataflow is a strong choice for users who need a flexible and scalable data processing solution. It is particularly well-suited for real-time and large-scale data processing tasks. However, the best choice ultimately depends on your specific requirements, including cost considerations, existing infrastructure, and technical skills.

Why this product is good

  • Google Cloud Dataflow is a fully managed service for stream and batch data processing. It is based on the Apache Beam model, allowing for a unified data processing approach. It is highly scalable, offers robust integration with other Google Cloud services, and provides powerful data processing capabilities. Its serverless nature means that users do not have to worry about infrastructure management, and it dynamically allocates resources based on the data processing needs.

Recommended for

  • Organizations that require real-time data processing.
  • Projects involving complex data transformations.
  • Users who already utilize Google Cloud Platform and need seamless integration with other Google services.
  • Developers and data engineers familiar with Apache Beam or those willing to learn.

JSON Server videos

+ Add

Angular CRUD with Web API Tutorial Part #3 - Setup Local JSON Server and Mock API Endpoints

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 JSON Server and Google Cloud Dataflow)

JSON Server

Google Cloud Dataflow

Development
100 100%
Development
0% 0
Big Data
0 0%
Big Data
100% 100
API Tools
100 100%
API Tools
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

Share your experience with using JSON Server 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 JSON Server and Google Cloud Dataflow

JSON Server Reviews

We have no reviews of JSON Server yet.
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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, JSON Server should be more popular than Google Cloud Dataflow. It has been mentiond 45 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.

JSON Server mentions (45)

  • Building a CRUD app with React Query, TypeScript, and Axios
    We'll be using json-server to create the REST API that we'll fetch data from. In the root of the project, create a db.json file with the contents. - Source: dev.to / about 1 year ago
  • Full Stack To Do list, a step-by-step tutorial
    Our backend will be little more than a two-way translation layer between the database and the user interface (UI). Later in this post we will identify other responsibilities of a backend but our implementation will be kept simple to demonstrate the fundamental machinery and concepts. It is worth noting the backend comes in two parts, web server and application server. Both json-server and Express are able to... - Source: dev.to / almost 2 years ago
  • Improve Frontend-Backend development harmony with JSON-Server
    JSON-Server creates fake REST API with a minimum amount of configuration, it provides a simple way to create mock RESTful APIs and easily define the required endpoints, allows easy definition of the data schema in a JSON file and can serve as a reference for each figure in the project. - Source: dev.to / about 2 years ago
  • Dictionary app
    I thought about usingJson Server (hosting the repo with the words on Github to begin with), Googlesheets, or maybe Firestore (i would prefer not to use it ,to avoid extra costs just in case it gets a reasonable amount of users). It isnt a big app so I just want a simple solution for storing the words and fetching them. Source: about 2 years ago
  • Playwright - Not just for Frontend
    First, I didn't create a backend API for this example, but I used a fake API to test. I created it with json-server and json-server-auth. They are two npm packages that use a JSON file as a database and expose the database in an API. You can find more about json-server in its documentation and about json-server-auth here. - Source: dev.to / over 2 years ago
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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 JSON Server and Google Cloud Dataflow, you can also consider the following products

JSON Placeholder - JSON Placeholder is a modern platform that provides you online REST API, which you can instantly use whenever you need any fake data.

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

mocki Fake JSON API - mocki Fake JSON API is an advanced platform that offers you to create API for personal use or testing purposes.

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

Mockae - The most flexible way to mock REST APIs with Lua code execution

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

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