Apache Spark vs Argyle Data comparison

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2,498 views|1,884 comparisons
89% willing to recommend
Argyle Data Logo
33 views|13 comparisons
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Apache Spark and Argyle Data based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.
To learn more, read our detailed Hadoop Report (Updated: April 2024).
767,667 professionals have used our research since 2012.
Featured Review
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
    Top Answer:We use Spark to process data from different data sources.
    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
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    Ranking
    1st
    out of 22 in Hadoop
    Views
    2,498
    Comparisons
    1,884
    Reviews
    25
    Average Words per Review
    432
    Rating
    8.7
    20th
    out of 22 in Hadoop
    Views
    33
    Comparisons
    13
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    Comparisons
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    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

    Argyle Data has had the privilege of working with global leaders and visionaries on their strategies for revenue threat analytics, big data, and machine learning. What consistently comes up is that best-in-class carriers know the revenue threats that they have been attacked with in the past. What they don’t know is how to prepare for future attacks that will likely incorporate new types and methods of revenue threats.

    What is critical to understand is that a) criminals are continually innovating; b) each subscriber will have many devices, many channels, and many potential attack points; and c) we need a better way to detect new fraud and protect customers and carriers in this new world – today in 2015, not in 2020.

    This requires an effective strategy for the use of big data and machine learning in the areas of:

    Fraud Threats

    Analytics apps for identifying threats from various types of domestic fraud and roaming fraud

    Profit Threats

    Analytics apps for identifying threats from arbitrage, negative margin, high usage, and bill shock

    SLA Threats

    Analytics apps for identifying threats from network vulnerabilities and from roaming partners not meeting their SLA windows

    Forensic Threats

    Graph analysis application for analyzing 1st to 5th degrees of separation between data assets



    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
    Cloudera, Gigamon, Hortonworks
    Top Industries
    REVIEWERS
    Computer Software Company30%
    Financial Services Firm15%
    University9%
    Marketing Services Firm6%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company13%
    Manufacturing Company7%
    Comms Service Provider6%
    No Data Available
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    No Data Available
    Buyer's Guide
    Hadoop
    April 2024
    Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: April 2024.
    767,667 professionals have used our research since 2012.

    Apache Spark is ranked 1st in Hadoop with 60 reviews while Argyle Data is ranked 20th in Hadoop. Apache Spark is rated 8.4, while Argyle Data 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 Cloudera Distribution for Hadoop, whereas Argyle Data is most compared with .

    See our list of best Hadoop vendors.

    We monitor all Hadoop 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.