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Meteor VS Apache Spark

Compare Meteor VS Apache Spark and see what are their differences

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Contents:
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  3. » Reviews
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Meteor logo Meteor

Meteor is a set of new technologies for building top-quality web apps in a fraction of the time.

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.
  • Meteor Landing page
    Landing page //
    2023-10-21
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Meteor features and specs

  • Full-Stack Solution
    Meteor offers an integrated full-stack solution, which includes both front-end and back-end development, making it easier to build and manage applications without needing disparate tools.
  • Reactive Programming
    Meteor leverages real-time data synchronization between the client and server, enabling reactive updates that automatically refresh the user interface when data changes.
  • MongoDB Integration
    Meteor tightly integrates with MongoDB, which facilitates real-time data integration and minimizes the complexity of database management.
  • Rich Ecosystem
    Meteor has a comprehensive ecosystem, including various plugins and packages that enhance functionality and help developers to quickly add features.
  • Developer Productivity
    Meteor emphasizes simplicity and productivity with features like hot code reload, which shortens the development feedback loop by updating the web page or app without a full refresh.
  • Strong Community
    Meteor has an active and supportive community, providing extensive documentation, tutorials, and forums that help developers troubleshoot and share knowledge.

Possible disadvantages of Meteor

  • Performance Issues
    For complex or large-scale applications, Meteor can face performance bottlenecks, especially around the use of MongoDB's oplog tailing for real-time data updates.
  • Single Database Limitation
    Meteor's default reliance on MongoDB can be a limitation for projects that would benefit from using other types of databases or require relational data structures.
  • Package Management
    While Meteor has a rich package ecosystem, it uses its own package manager, which can sometimes lead to compatibility issues or limit the ability to use NPM packages directly.
  • Learning Curve
    Though designed to be easy to use, Meteor’s unique concepts and full-stack nature can present a learning curve for developers who are not familiar with JavaScript or full-stack development.
  • Lack of Control
    Meteor's high level of abstraction can be a double-edged sword, making it difficult for developers to optimize certain aspects of their application or have fine-grained control over performance.
  • Community Shifts
    The Meteor community has experienced shifts and changes since its inception, and there have been periods of uncertainty regarding its long-term viability and support.

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.

Meteor videos

+ Add

The Meteor | Discraft Disc Review

More videos:

  • Review - Meteor Review - with Tom Vasel
  • Review - Royal Enfield Meteor 350 | Meteor 350 | Next Generation Royal Enfield Thunderbird | Review by Aj

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 Meteor and Apache Spark)

Meteor

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

Share your experience with using Meteor and Apache Spark. 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 Meteor and Apache Spark

Meteor Reviews

20 Next.js Alternatives Worth Considering
Exploring Next.js alternatives can open up a world of possibilities for web development projects. Choosing from frameworks like Gatsby.js, Nuxt.js, or Svelte can offer tailored features for server-side rendering, single-page applications (SPAs), and static site generation. Each option has its strengths, whether you’re aiming for speed with Hugo, ease of use with Jekyll, or...
Source: tms-outsource.com
The 20 Best Laravel Alternatives for Web Development
Meteor — a full-stack platform that’s got every stage of your app covered. Real-time by default, it’s about in-sync, on-the-fly updates across client and server. Magic? Feels like it.
Source: tms-outsource.com
9 Best JavaScript Frameworks to Use in 2023
Meteor.js is a JavaScript-based platform for developing web applications. It’s open source and supports various programming paradigms, including object-oriented, functional, and event-driven programming. Meteor.js is based on the Node.js framework and uses an asynchronous programming model.
Source: ninetailed.io
20 Best JavaScript Frameworks For 2023
Meteor.js, also known as Meteor, is a Node.js-based isomorphic JavaScript web framework that is partially commercial but primarily free and open-source. Meteor simplifies real-time app development by providing a complete ecosystem rather than requiring multiple tools and frameworks to achieve the same result.
Source: www.lambdatest.com
Top 10 Best Node. Js Frameworks to Improve Web Development
It is a pretty fundamental full-stack Node.js method for creating mobile web applications. It is an ideal one and works with iOS, Android, or web desktop. Also, Meteor too executes application progress very prepared by allowing a platform for the entire development of the web application to continue in the corresponding language, none other than JavaScript.
Source: www.mxicoders.com

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, Apache Spark should be more popular than Meteor. It has been mentiond 70 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.

Meteor mentions (12)

  • Reactive Data Structures in MeteorJS - Reactive Stack
    MeteorJS brings client-side reactivity out of the box. No matter which frontend framework you choose, you will always have an integrated reactivity that synchronizes your data and the UI. This is one of the core strengths of MeteorJS. - Source: dev.to / 6 months ago
  • MeteorJS 3.0 major impact estimated for July 2024 ☄️ - here is all you need to know 🧐
    The next major MeteorJS release is coming in July 2024! After more than two years of development, this is the final result. The first discussions started in June 2021 and there has been multiple alphas, betas, rcs and a huge amount of package updates. These were constantly battle-tested by the Meteor Core team and the Community, shaping the features and performance of the platform one by one. - Source: dev.to / 11 months ago
  • Tutorial: how to install Meteor.js with Tailwind CSS and Flowbite
    Meteor.js is a full-stack JavaScript platform for developing modern web and mobile applications. Meteor includes a key set of technologies for building connected-client reactive applications, a build tool, and a curated set of packages from the Node.js and general JavaScript community. - Source: dev.to / almost 2 years ago
  • Meteor.js with Vite, Solid, and Tailwind CSS
    Meteor.js is a full-stack platform that simplifies the development of web applications by providing a unified approach to building both the front-end and back-end. With real-time data updates, Meteor.js speeds up the development process and ensures you can create powerful applications. - Source: dev.to / about 2 years ago
  • If You Were Building Chess.com Today, Which Tech Stack Would You Use and Why?
    You could build the whole thing with meteor.com and React. Source: over 2 years 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 / 20 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 / 21 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
  • 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 / 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 Meteor and Apache Spark, you can also consider the following products

ExpressJS - Sinatra inspired web development framework for node.js -- insanely fast, flexible, and simple

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

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

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

Django - The Web framework for perfectionists with deadlines

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

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