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Hadoop VS Spark Framework

Compare Hadoop VS Spark Framework and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing

Spark Framework logo Spark Framework

Spark Framework is a simple and lightweight Java web framework built for rapid development.
  • Hadoop Landing page
    Landing page //
    2021-09-17
  • Spark Framework Landing page
    Landing page //
    2019-11-24

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.

Spark Framework features and specs

  • Ease of Use
    Spark Framework provides a simple and intuitive API, making it easy to set up and run a web application with minimal configuration.
  • Lightweight
    Spark is very lightweight, which makes it well-suited for small applications and microservices where resource consumption is a concern.
  • Java 8 Lambda Support
    It supports Java 8 lambdas, allowing developers to write clean, readable, and more concise code.
  • Rapid Development
    The framework facilitates rapid development and prototyping, enabling developers to quickly build and iterate on ideas.
  • Minimal Configuration
    With less boilerplate code required, Spark allows developers to focus on business logic rather than intricate configurations.

Possible disadvantages of Spark Framework

  • Limited Ecosystem
    Compared to more established frameworks, Spark has a smaller ecosystem of plugins and extensions, which might limit functionality for larger projects.
  • Performance Overhead
    While suitable for small applications, the simplicity of Spark might introduce performance overhead when scaling up to larger, complex applications.
  • Concurrency Limitations
    Its concurrency model may not be robust enough for high-concurrency applications, potentially leading to scalability issues.
  • Less Community Support
    Spark's smaller user base means that community support and resources such as tutorials and forums are more limited compared to larger frameworks.
  • Basic Feature Set
    The framework offers a basic feature set, which may require additional coding or third-party libraries to achieve advanced functionalities.

Hadoop videos

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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

Spark Framework videos

No Spark Framework videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Hadoop and Spark Framework)

Hadoop

Spark Framework

Databases
100 100%
Databases
0% 0
Web Frameworks
0 0%
Web Frameworks
100% 100
Big Data
100 100%
Big Data
0% 0
Developer Tools
0 0%
Developer Tools
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 Hadoop and Spark Framework

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

Spark Framework Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
You can get the Spark Framework up and running in just a few minutes. By default, it runs on the Jetty web server that is embedded into the framework. However, you can use it with other Java web servers as well. According to Spark’s own survey, more than 50% of their users used the framework to create REST APIs, which is its most popular use case. Spark also powers...
Source: raygun.com

Social recommendations and mentions

Spark Framework 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.

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 / about 14 hours 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 / about 15 hours 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 / 7 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

Spark Framework mentions (29)

  • Indexing All of Wikipedia on a Laptop
    The code for serving queries is found in the WebSearch class. We’re using Spark (the web framework, not the big data engine) to serve a simple search form:. - Source: dev.to / 11 months ago
  • [ Servlet + JSP + JDBC ]
    Get a solid grasp of building web applications with Java either using Spring (using Spring Boot) or Spark (if you're also new to Java learning Java and Spring can be a mouthful). Instead of JSP use something Thymeleaf or build the frontend with HTML and JavaScript (and serve the bundles). Source: over 1 year ago
  • What's the language of the startup?
    So most of the "tech" stack goes out. In our first startup we created our own web-container by using https://sparkjava.com - and then built a JSR-223 scripting support. Source: over 1 year ago
  • What side-projects did you work on during your university years?
    Stack: Java, Spark (not the Apache Spark but this), Kafka, several other libraries like FasterXML's Jackson. Source: almost 2 years ago
  • Full Time
    The blog is just hugo so it's 100% static files over nginx. The search engine is serverside-rendered mustache templates via handlebars[1], via served via spark[2]. It's basically all vanilla Java. I do raw SQL queries instead of ORM, which makes it quite a bit snappier than most Java applications. The sheer size of the database also mandates that basically every query is a primary key lookup. The code is written... - Source: Hacker News / almost 2 years ago
View more

What are some alternatives?

When comparing Hadoop and Spark Framework, you can also consider the following products

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.

vert.x - From Wikipedia, the free encyclopedia

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

Micronaut Framework - Build modular easily testable microservice & serverless apps

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