JethroData vs Spark SQL comparison

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55 views|42 comparisons
Apache Logo
1,534 views|1,005 comparisons
85% willing to recommend
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  • "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."
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    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
    2nd
    out of 8 in BI on Hadoop
    Views
    55
    Comparisons
    42
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    4th
    out of 22 in Hadoop
    Views
    1,534
    Comparisons
    1,005
    Reviews
    7
    Average Words per Review
    543
    Rating
    8.3
    Comparisons
    Learn More
    Overview
    Jethro is a performance acceleration engine that delivers interactive Business Intelligence (BI) on Big Data at Hadoop costs. Jethro accelerates Big Data performance with Tableau, Qlik, MicroStrategy as well as in-house BI applications. - Interactive Performance: Jethro delivers response in seconds on 10s of billions of rows—on any SQL query - High Concurrency: 1,000s of concurrent users can access the same data without impacting performance. - No Data Engineering: Jethro automatically handles all of the costly, ineffective and endless data engineering Jethro is data source agnostic and is compatible with any Hadoop distribution, AWS, EFS, Azure and almost any other data source.
    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
    Avis, Tata Communications, Fiat Chrysler, BICS, Symphony Health, iBasis, Data Realty, Fortune 100 Bank, Large Big Box US Retailer
    UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
    Top Industries
    No Data Available
    VISITORS READING REVIEWS
    Financial Services Firm21%
    Computer Software Company14%
    University8%
    Manufacturing Company5%
    Company Size
    No Data Available
    REVIEWERS
    Small Business36%
    Midsize Enterprise43%
    Large Enterprise21%
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise13%
    Large Enterprise74%

    JethroData is ranked 2nd in BI on Hadoop while Spark SQL is ranked 4th in Hadoop with 14 reviews. JethroData 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". JethroData 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.

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