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Apache Spark vs Vert.x comparison

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Apache Spark

Read 60 Apache Spark reviews
4,283 views|3,325 comparisons
89% willing to recommend
Eclipse Foundation Logo

Vert.x

2,061 views|1,750 comparisons
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Buyer's Guide
Java Frameworks
March 2024
Executive Summary

We performed a comparison between Apache Spark and Vert.x based on real PeerSpot user reviews.

Find out what your peers are saying about VMware, Apache, Eclipse Foundation and others in Java Frameworks.
To learn more, read our detailed Java Frameworks Report (Updated: March 2024).
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Featured Review
Vineeth Marar
Offers seamless integration with Azure services and on-premises servers
We've set up a Spark cluster running in Azure to process real-time data. This setup involves connecting Azure applications to the Spark cluster via... Read more →
Use Vert.x?
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pricing and Cost Advice
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
  • "We are using the free version of the solution."
  • "Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."
  • "Apache Spark is an expensive solution."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
  • "It is an open-source solution, it is free of charge."

    • More Apache Spark Pricing and Cost Advice →

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    Questions from the Community
    What do you like most about Apache Spark?
    Top Answer:We use Spark to process data from different data sources.
    Read all 30 answers   →
    What is your experience regarding pricing and costs for Apache Spark?
    Top Answer:The solution is moderately priced.
    Read all 19 answers   →
    What needs improvement with Apache Spark?
    Top Answer:In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, and do the transformation in a subsecond
    Read all 32 answers   →
    Ask a question

    Earn 20 points

    Ranking
    2nd
    out of 12 in Java Frameworks
    Views
    4,283
    Comparisons
    3,325
    Reviews
    25
    Average Words per Review
    432
    Rating
    8.7
    9th
    out of 12 in Java Frameworks
    Views
    2,061
    Comparisons
    1,750
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    Comparisons
    Spring Boot logo
    Spring Boot vs. Apache Spark
    Compared 31% of the time.
    AWS Batch logo
    AWS Batch vs. Apache Spark
    Compared 10% of the time.
    Spark SQL logo
    Spark SQL vs. Apache Spark
    Compared 10% of the time.
    SAP HANA logo
    SAP HANA vs. Apache Spark
    Compared 8% of the time.
    More Apache Spark Competitors   →
    Spring Boot logo
    Spring Boot vs. Vert.x
    Compared 74% of the time.
    Jakarta EE logo
    Jakarta EE vs. Vert.x
    Compared 9% of the time.
    Eclipse MicroProfile logo
    Eclipse MicroProfile vs. Vert.x
    Compared 4% of the time.
    Spring MVC logo
    Spring MVC vs. Vert.x
    Compared 4% of the time.
    More Vert.x Competitors   →
    Learn More
    Apache
    Eclipse Foundation
    Overview

    Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

    Vert. x is an open source, reactive and polyglot software development toolkit from the developers of Eclipse. Reactive programming is a programming paradigm, associated with asynchronous streams, which respond to any changes or events. Similarly, Vert.

    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    hopscotch, liferay, zalando, ticketmaster, swisscom, tesco
    Top Industries
    REVIEWERS
    Computer Software Company30%
    Financial Services Firm15%
    University9%
    Marketing Services Firm6%
    VISITORS READING REVIEWS
    Financial Services Firm24%
    Computer Software Company13%
    Manufacturing Company7%
    Comms Service Provider6%
    VISITORS READING REVIEWS
    Financial Services Firm33%
    Computer Software Company15%
    Comms Service Provider9%
    Government4%
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise12%
    Large Enterprise73%
    Buyer's Guide
    Java Frameworks
    March 2024
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    Apache Spark is ranked 2nd in Java Frameworks with 60 reviews while Vert.x is ranked 9th in Java Frameworks. Apache Spark is rated 8.4, while Vert.x is rated 0.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and Informatica Big Data Parser, whereas Vert.x is most compared with Spring Boot, Jakarta EE, Eclipse MicroProfile and Spring MVC.

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    We monitor all Java Frameworks reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.