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Pepperdata vs Spark SQL comparison

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Pepperdata

57 views|51 comparisons
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Spark SQL

Read 14 Spark SQL reviews
1,534 views|1,005 comparisons
85% willing to recommend
Comparison Buyer's Guide
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Buyer's Guide
Application Performance Monitoring (APM) and Observability
April 2024
Executive Summary

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

Find out what your peers are saying about Datadog, Dynatrace, New Relic and others in Application Performance Monitoring (APM) and Observability.
To learn more, read our detailed Application Performance Monitoring (APM) and Observability Report (Updated: April 2024).
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767,847 professionals have used our research since 2012.
Featured Review
Use Pepperdata?
Sahil Taneja
Easy to use and do not require a learning curve
We are using Spark SQL in Databricks. There are three ways to code: Task Equals, PySpark, and Scala. Some team members are also using Spark SQL.... Read more →
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 →

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    Questions from the Community
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    What do you like most about Spark SQL?
    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.
    Read all 14 answers   →
    What is your experience regarding pricing and costs for Spark SQL?
    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.
    Read all 8 answers   →
    What needs improvement with Spark SQL?
    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 »
    Read all 14 answers   →
    Ranking
    101st
    out of 111 in Application Performance Monitoring (APM) and Observability
    Views
    57
    Comparisons
    51
    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
    Pepperdata
    Apache
    Overview

    As big data stacks increase in scope and complexity, most data-driven organizations understand that automation and observability are necessary for modern real-time big data performance management. Without automation and observability, engineers and developers cannot optimize or ensure application and infrastructure performance, or keep cost under control. Pepperdata helps some of the most successful companies in the world manage their big data performance in the cloud and in the data center. These customers choose and trust Pepperdata because of three key product differentiators: autonomous optimization, full-stack observability, and cost optimization.

    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, Hortonworks, IBM, MapR
    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%
    Buyer's Guide
    Application Performance Monitoring (APM) and Observability
    April 2024
    Download Free Report
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    767,847 professionals have used our research since 2012.

    Pepperdata is ranked 101st in Application Performance Monitoring (APM) and Observability while Spark SQL is ranked 4th in Hadoop with 14 reviews. Pepperdata 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". Pepperdata 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.

    We monitor all Application Performance Monitoring (APM) and Observability 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.