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Ruby on Rails VS Apache Spark

Compare Ruby on Rails VS Apache Spark and see what are their differences

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Ruby on Rails logo Ruby on Rails

Ruby on Rails is an open source full-stack web application framework for the Ruby programming...

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Ruby on Rails Landing page
    Landing page //
    2023-10-23

We recommend LibHunt Ruby for discovery and comparisons of trending Ruby projects. Also, to find more open-source ruby alternatives, you can check out libhunt.com/r/rails

  • Apache Spark Landing page
    Landing page //
    2021-12-31

Ruby on Rails features and specs

  • Rapid Development
    Ruby on Rails uses conventions over configurations which allows developers to build applications quickly. It comes with a wealth of built-in tools and libraries that streamline the development process.
  • Community Support
    Rails has a vibrant and active community. This means a lot of third-party libraries (gems) are available, and you can easily find help and resources.
  • Convention over Configuration
    Rails emphasizes convention over configuration, which reduces the number of decisions developers need to make. This can increase productivity and consistency across projects.
  • Built-in Testing
    Rails comes with a strong built-in testing framework, making it easier to test your application and ensure that it works as expected.
  • Scalability Options
    Although it has a reputation for not being the most scalable framework, Rails can be made scalable with good architecture and the right tools.
  • RESTful Design
    Rails promotes RESTful application design, which means that it aligns well with best practices in web development and makes it easier to build APIs.

Possible disadvantages of Ruby on Rails

  • Performance
    Ruby on Rails can be slower than some other frameworks, particularly for applications that require a lot of computation or have high traffic.
  • Learning Curve
    While Rails makes many things easier with its conventions, this can create a steep learning curve for newcomers who need to understand the 'Rails way' of doing things.
  • Scalability Concerns
    Due to its monolithic nature, scaling Rails can be challenging, requiring significant architectural changes and optimizations.
  • Lesser Flexibility
    The conventions that make Rails easy to use can also be limiting. When you need to do something outside the typical Rails flow, it may be harder to implement.
  • Runtime Speed
    Ruby, the language that Rails is built on, is generally slower in terms of execution speed compared to other languages like Java or C++.
  • Memory Consumption
    Rails applications can consume a lot of memory, which can be a concern for large-scale applications or those with limited resources.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Ruby on Rails videos

+ Add

Ruby On Rails Biggest Waste Of Time In 2020 | Ruby on Rails Dead

More videos:

  • Tutorial - Ruby on Rails Tutorial | Build a Book Review App - Part 1

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Ruby on Rails and Apache Spark)

Ruby on Rails

Apache Spark

Developer Tools
100 100%
Developer Tools
0% 0
Databases
0 0%
Databases
100% 100
Web Frameworks
100 100%
Web Frameworks
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 Ruby on Rails and Apache Spark

Ruby on Rails Reviews

  1. Stan
    · Founder at SaaSHub ·
    The most productive web framework

    Yes, there are other more trending frameworks; however, nothing reaches the productivity of Rails. It's simply unbeatable if you have a small team.

    For example both SaaSHub and LibHunt were built on Rails.

    🏁 Competitors: Django, Laravel
Top 9 best Frameworks for web development
The best frameworks for web development include React, Angular, Vue.js, Django, Spring, Laravel, Ruby on Rails, Flask and Express.js. Each of these frameworks has its own advantages and distinctive features, so it is important to choose the framework that best suits the needs of your project.
Source: www.kiwop.com
Top 5 Laravel Alternatives
In terms of documentation, guidelines, and libraries, Ruby on Rails is the superior framework for smaller applications. Since it entered the online scene before Laravel, its community is larger and more well-liked among programmers. When compared to other Laravel alternatives, Ruby’s code is much simpler to understand and write.
Source: www.etatvasoft.com
Top 10 Phoenix Framework Alternatives
While modern frameworks try to minimize the tradeoffs to a limited extent, none of them has come closer to the implementation of the Phoenix Framework, which offers Ruby on Rails levels of productivity while being one of the fastest frameworks available in the market.
Source: blog.back4app.com
10 Ruby on Rails Alternatives For Web Development in 2022
Once a prolific web development technology, in 2021, both Ruby and Ruby on Rails are considered dying technologies. The data speaks for itself. In October 2021, Ruby lost 3 ranks in the Tiobe Index compared to October 2020 and became the 16th most searched programming language. The same decline in Ruby on Rails popularity is demonstrated by Google Trends. The language...
Source: www.ideamotive.co
Get Over Ruby on Rails — 3 Alternative Web Frameworks Worth Checking Out
Disclaimer: I started working on this article before the big controversy about Basecamp happened. I don’t want to make any point about this in the article. Regardless of what DHH and others are saying on different topics, Ruby on Rails is still a great piece of software and will continue to be. But there are some great alternatives as well that I would like to highlight.
Source: betterprogramming.pub

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Ruby on Rails should be more popular than Apache Spark. It has been mentiond 142 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.

Ruby on Rails mentions (142)

  • Unlocking Opportunities: How to Thrive as a Ruby Engineer in Today's Tech Landscape
    Ruby on Rails open source projects. Contribute and learn at the same time. - Source: dev.to / 1 day ago
  • Open Source: A Goldmine for Indie Hackers
    Speed of Development: Frameworks such as Django or Rails accelerate the development process. - Source: dev.to / 2 days ago
  • Indie Hacking with Open Source Tools: Innovating on a Budget
    This ecosystem is fueled by repositories hosting powerful languages, functions, and versatile tools—from backend frameworks like Django and Ruby on Rails to containerization with Docker and distributed version control via Git. Moreover, indie hackers can also utilize open source design tools (e.g. GIMP, Inkscape) and analytics platforms such as Matomo. - Source: dev.to / 4 days ago
  • Charybdis ORM: Building High-Performance Distributed Rust Backends with ScyllaDB
    Ruby on Rails (RoR) is one of the most renowned web frameworks. When combined with SQL databases, RoR transforms into a powerhouse for developing back-end (or even full-stack) applications. It resolves numerous issues out of the box, sometimes without developers even realizing it. For example, with the right callbacks, complex business logic for a single API action is automatically wrapped within a transaction,... - Source: dev.to / 14 days ago
  • Ask HN: What's the ideal stack for a solo dev in 2025
    As it's just you I'd stick with Ruby on Rails 8[1] as you already know it and I think it could realistically easily achieve what you're proposing. There's lots of libraries to for calling out external AI services. e.g. Something like FastMCP[2] From the sound of it that's all you need. I'd use Hotwire[3] for the frontend and Hotwire Native if you want to rollout an app version quickly. I'd back it with... - Source: Hacker News / about 1 month ago
View more

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 16 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 18 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 / about 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / about 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing Ruby on Rails and Apache Spark, you can also consider the following products

Laravel - A PHP Framework For Web Artisans

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

Django - The Web framework for perfectionists with deadlines

Hadoop - Open-source software for reliable, scalable, distributed computing

ASP.NET - ASP.NET is a free web framework for building great Web sites and Web applications using HTML, CSS and JavaScript.

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

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