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vert.x VS Hadoop

Compare vert.x VS Hadoop and see what are their differences

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vert.x logo vert.x

From Wikipedia, the free encyclopedia

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing
  • vert.x Landing page
    Landing page //
    2022-06-12
  • Hadoop Landing page
    Landing page //
    2021-09-17

vert.x features and specs

  • Performance
    Vert.x is designed to be highly performant, leveraging a non-blocking, event-driven architecture which makes it suitable for handling many concurrent requests efficiently.
  • Polyglot
    Vert.x supports multiple programming languages, including Java, Kotlin, JavaScript, Groovy, Ruby, and more. This allows developers to use the language they are most comfortable with.
  • Modular
    Vert.x is modular and lightweight, enabling developers to use only the parts they need and easily integrate with other libraries and tools.
  • Reactive Ecosystem
    Vert.x provides a robust ecosystem for building reactive applications, including asynchronous APIs, event bus, and reactive streams.
  • Scalability
    The architecture of Vert.x allows for easy scaling both vertically and horizontally, as it can efficiently manage resources and load balancing.

Possible disadvantages of vert.x

  • Learning Curve
    The event-driven and asynchronous nature of Vert.x can be challenging for developers who are accustomed to traditional synchronous programming paradigms.
  • Community and Resources
    While growing, the Vert.x community is smaller compared to more established frameworks, which may result in fewer resources, tutorials, and third-party integrations.
  • Complexity
    As applications grow in size, managing asynchronous code and callback structures can become complex, requiring careful planning and architecture decisions.
  • Tooling
    Tooling support, while improving, may not be as comprehensive as other established frameworks, which might impact development speed and debugging.

Hadoop features and specs

  • Scalability
    Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
  • Cost-Effective
    It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
  • Fault Tolerance
    Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
  • Flexibility
    It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
  • Parallel Processing
    Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
  • Community Support
    As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

  • Complexity
    Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
  • Overhead
    The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
  • Security
    While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
  • Hardware Requirements
    Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
  • Lack of Real-Time Processing
    Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
  • Data Integrity
    Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

vert.x videos

+ Add

From Zero to Back End in 45 Minutes with Eclipse Vert.x

Hadoop videos

+ Add

What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

Category Popularity

0-100% (relative to vert.x and Hadoop)

vert.x

Hadoop

Web Frameworks
100 100%
Web Frameworks
0% 0
Databases
0 0%
Databases
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare vert.x and Hadoop

vert.x Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
As Vert.x is an event-driven and non-blocking framework, it can handle a lot of concurrencies using only a minimal number of threads. Vert.x is also quite lightweight, with the core framework weighing only about 650 KB. It has a modular architecture that allows you to use only the modules you need so that your app can stay as slick as possible. Vert.x is an ideal choice if...
Source: raygun.com

Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
Source: www.datacamp.com
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
Source: www.analytixlabs.co.in
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is...
Source: www.analyticsinsight.net

Social recommendations and mentions

vert.x might be a bit more popular than Hadoop. We know about 29 links to it since March 2021 and only 25 links to Hadoop. 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.

vert.x mentions (29)

  • Java News: WildFly 36, Spring Milestones, and Open Liberty Updates
    The sixth release candidate of Eclipse Vert.x 5.0.0 provides support for the Java Platform Module System and a new VerticleBase class. Further details are available in the release notes. - Source: dev.to / 28 days ago
  • Rust, C++, and Python trends in jobs on Hacker News (February 2025)
    I see your point, but I still don't think you can just say "If you want to get get a job as a Go developer, you must know gRPC." Even more so for Kafka, I've only heard about it being popular in the Java world. You can't even say "If you want to get a job as a Java developer, you must know Spring." Nowadays, sane Java projects use https://vertx.io, it's just too good. I would argue that Spring is for legacy... - Source: Hacker News / 3 months ago
  • Error handlers and failure handlers in Vert.x
    Vert.x is a toolkit for developing reactive applications on the JVM. I wrote a short introductory post about it earlier, when I used it for a commercial project. I had to revisit a Vert.x-based hobby project a few weeks ago, and I learned that there were some gaps in my knowledge about how Vert.x handles failures and errors. To fill those gaps, I did some experiments, wrote a few tests, and then wrote this blog post. - Source: dev.to / 6 months ago
  • Spark – A web micro framework for Java and Kotlin
    Https://vertx.io/ It's actively maintained with full time developers, performant, supports Kotlin out of the box, and has more features? - Source: Hacker News / over 1 year ago
  • Reactive database access on the JVM
    Hibernate Reactive integrates with Vert.x, but an extension allows to bridge to Project Reactor if wanted. - Source: dev.to / almost 2 years ago
View more

Hadoop mentions (25)

  • Apache Hadoop: Open Source Business Model, Funding, and Community
    This post provides an in‐depth look at Apache Hadoop, a transformative distributed computing framework built on an open source business model. We explore its history, innovative open funding strategies, the influence of the Apache License 2.0, and the vibrant community that drives its continuous evolution. Additionally, we examine practical use cases, upcoming challenges in scaling big data processing, and future... - Source: dev.to / 3 days ago
  • What is Apache Kafka? The Open Source Business Model, Funding, and Community
    Modular Integration: Thanks to its modular approach, Kafka integrates seamlessly with other systems including container orchestration platforms like Kubernetes and third-party tools such as Apache Hadoop. - Source: dev.to / 3 days ago
  • India Open Source Development: Harnessing Collaborative Innovation for Global Impact
    Over the years, Indian developers have played increasingly vital roles in many international projects. From contributions to frameworks such as Kubernetes and Apache Hadoop to the emergence of homegrown platforms like OpenStack India, India has steadily carved out a global reputation as a powerhouse of open source talent. - Source: dev.to / 9 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • Apache Hadoop: Pioneering Open Source Innovation in Big Data
    Apache Hadoop is more than just software—it’s a full-fledged ecosystem built on the principles of open collaboration and decentralized governance. Born out of a need to process vast amounts of information efficiently, Hadoop uses a distributed file system and the MapReduce programming model to enable scalable, fault-tolerant computing. Central to its success is a diverse ecosystem that includes influential... - Source: dev.to / 2 months ago
View more

What are some alternatives?

When comparing vert.x and Hadoop, you can also consider the following products

Micronaut Framework - Build modular easily testable microservice & serverless apps

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

Javalin - Simple REST APIs for Java and Kotlin

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

helidon - Helidon Project, Java libraries crafted for Microservices

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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