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Babel VS Apache Spark

Compare Babel VS Apache Spark and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Babel logo Babel

Babel is a compiler for writing next generation JavaScript.

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.
  • Babel Landing page
    Landing page //
    2023-04-02
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Babel features and specs

  • JavaScript Version Compatibility
    Babel allows developers to write code using the latest JavaScript features and syntax, and transpile it into a version of JavaScript that can run on older browsers. This ensures greater compatibility across different environments.
  • Future-Proof Code
    With Babel, developers can start using upcoming JavaScript features today. This means that codebases can stay modern and developers can take advantage of new functionalities without waiting for full browser support.
  • Ecosystem and Plugins
    Babel has a rich ecosystem of plugins and presets that can extend its capabilities, making it highly adaptable to different project needs. This modularity allows for customization and enhancement of the build process.
  • Integration with Modern Development Tools
    Babel integrates well with various development tools such as Webpack, making it easier to include in existing build processes and workflows. This helps streamline development and maintain efficient workflows.
  • Community and Support
    Babel has a large and active community, which means extensive documentation, tutorials, and support forums. This can be particularly useful for troubleshooting and staying updated with best practices.

Possible disadvantages of Babel

  • Performance Overhead
    Transpiling code with Babel introduces a performance overhead during the build process. This can slow down development workflows, especially for large codebases with many files.
  • Configuration Complexity
    Setting up Babel can be complex, particularly for beginners. The numerous options and plugins available can sometimes be overwhelming and require significant time to configure correctly.
  • Source Map Issues
    Generating accurate source maps can sometimes be tricky with Babel, leading to difficulties in debugging. Misconfigured source maps can make it harder to track down issues within the original source code.
  • Dependency Bloat
    Including Babel in a project can add a significant number of dependencies. This dependency bloat can increase the size of the project and potentially introduce maintenance challenges or security vulnerabilities.
  • Learning Curve
    There is a learning curve associated with Babel, especially for developers who are new to modern JavaScript tooling. Understanding how Babel works and how to effectively use its features can take time and effort.

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.

Babel videos

+ Add

Babel - Movie Review

More videos:

  • Review - Day 16 | Babel Review | 365 Films
  • Review - Worth The Hype? - BABEL Review
  • Review - Book CommuniTEA: Is BABEL a rac1st mani!fest0? [you should know the answer]
  • Review - Babel is a Masterpiece, And Here's Why

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 Babel and Apache Spark)

Babel

Apache Spark

Development Tools
100 100%
Development Tools
0% 0
Databases
0 0%
Databases
100% 100
Javascript UI Libraries
100 100%
Javascript UI Libraries
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 Babel and Apache Spark

Babel Reviews

We have no reviews of Babel yet.
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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, Babel should be more popular than Apache Spark. It has been mentiond 147 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.

Babel mentions (147)

  • Valentine’s Day Breakup: React Dumps Create React App
    Create React App (CRA) is a command-line interface tool that allows developers to set up a React project easily. It primarily serves as a project scaffolding tool, allowing you to create a new project with a single command: npx create-react-app . CRA comes with tools like Webpack and Babel, which handle the bundling and transpiling of code. The tools are pre-configured. It comes with a development server that... - Source: dev.to / about 2 months ago
  • #wecode Landing Page - WeCoded Challenge March 2025
    @vitejs/plugin-react uses Babel for Fast Refresh. - Source: dev.to / 3 months ago
  • You Don’t Know JS Yet: My Weekly Journey Through JavaScript Mastery
    For new and incompatible syntax, the solution is transpiling—converting newer JS syntax to older syntax that can run on older engines. The most popular transpiler? Babel. This process ensures modern JS code can still reach a wide audience, even on legacy systems. - Source: dev.to / 3 months ago
  • Desktop apps for Windows XP in 2025
    Fortunately we have tools like PostCSS and Babel, that let you target your specific Browser version, and they'll do their best to transpile and polyfill your code to work with that version. This alone will do a lot of the heavy lifting for you if you are working with a lot of code. However, if you are just writing out a few HTML, CSS, and JS files, then that would be overkill and you can just figure out what code... - Source: dev.to / 3 months ago
  • The Tools and APIs That Made My GeoGuessr 🌍 Project Possible
    Cross-Browser Compatibility: Some features worked differently across browsers. I used Babel to transpile my JavaScript code, ensuring it worked consistently everywhere. - Source: dev.to / 4 months 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 / 30 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 / about 1 month 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 Babel and Apache Spark, you can also consider the following products

jQuery - The Write Less, Do More, JavaScript Library.

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

React Native - A framework for building native apps with React

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

Composer - Composer is a tool for dependency management in PHP.

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

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