Argyle Data vs Spark SQL comparison

Cancel
You must select at least 2 products to compare!
Argyle Data Logo
32 views|13 comparisons
Apache Logo
1,569 views|1,005 comparisons
Comparison Buyer's Guide
Executive Summary

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

Find out what your peers are saying about Cloudera, Apache, Amazon and others in Hadoop.
To learn more, read our detailed Hadoop Report (Updated: March 2024).
765,386 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
Information Not Available
  • "The solution is open-sourced and free."
  • "There is no license or subscription for this solution."
  • "The solution is bundled with Palantir Foundry at no extra charge."
  • "The on-premise solution is quite expensive in terms of hardware, setting up the cluster, memory, hardware and resources. It depends on the use case, but in our case with a shared cluster which is quite large, it is quite expensive."
  • "We use the open-source version, so we do not have direct support from Apache."
  • "We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small."
  • More Spark SQL Pricing and Cost Advice →

    report
    Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
    765,386 professionals have used our research since 2012.
    Questions from the Community
    Ask a question

    Earn 20 points

    Top Answer:Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.
    Top Answer:We don't have to pay for licenses with this solution because we are working in a small market, and we rely on open-source because the budgets of projects are very small.
    Top Answer:In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working… more »
    Ranking
    20th
    out of 22 in Hadoop
    Views
    32
    Comparisons
    13
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    4th
    out of 22 in Hadoop
    Views
    1,569
    Comparisons
    1,005
    Reviews
    7
    Average Words per Review
    543
    Rating
    8.3
    Comparisons
    Learn More
    Overview

    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



    Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. There are several ways to interact with Spark SQL including SQL and the Dataset API. When computing a result the same execution engine is used, independent of which API/language you are using to express the computation. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.
    Sample Customers
    Cloudera, Gigamon, Hortonworks
    UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
    Top Industries
    No Data Available
    VISITORS READING REVIEWS
    Financial Services Firm21%
    Computer Software Company14%
    University8%
    Construction Company6%
    Company Size
    No Data Available
    REVIEWERS
    Small Business36%
    Midsize Enterprise43%
    Large Enterprise21%
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise13%
    Large Enterprise74%
    Buyer's Guide
    Hadoop
    March 2024
    Find out what your peers are saying about Cloudera, Apache, Amazon and others in Hadoop. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Argyle Data is ranked 20th in Hadoop while Spark SQL is ranked 4th in Hadoop with 14 reviews. Argyle Data is rated 0.0, while Spark SQL is rated 7.8. On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". Argyle Data is most compared with , whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics.

    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.