Hortonworks Data Platform vs Spark SQL comparison

Cancel
You must select at least 2 products to compare!
Comparison Buyer's Guide
Executive Summary

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

Find out in this report how the two Hadoop solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Hortonworks Data Platform vs. Spark SQL 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:
Pros
"Ambari Web UI: user-friendly.""The data platform is pretty neat. The workflow is also really good.""It is a scalable platform.""Hortonworks should not be expensive at all to those looking into using it.""The Hortonworks solution is so stable. It is working as a production system, without any error, without any downtime. If I have downtime, it is mostly caused by the hardware of the computers.""Now, using this solution, it is much cheaper to have all of the data available for searching, not in real-time, but whenever there is a pending request.""Ranger for security; with Ranger we can manager user’s permissions/access controls very easily.""The upgrades and patches must come from Hortonworks."

More Hortonworks Data Platform Pros →

"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks.""Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.""The speed of getting data.""The team members don't have to learn a new language and can implement complex tasks very easily using only SQL.""The performance is one of the most important features. It has an API to process the data in a functional manner.""This solution is useful to leverage within a distributed ecosystem.""Overall the solution is excellent.""Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."

More Spark SQL Pros →

Cons
"More information could be there to simplify the process of running the product.""Hive performance. If Hive performance increased, Hadoop would replace (not everywhere) traditional databases.""Security and workload management need improvement.""The cost of the solution is high and there is room for improvement.""Since Cloudera acquired HDP, it's been bundled with CBH and HDP. However, the biggest challenge is cloud storage integration with Azure, GCP, and AWS.""I would like to see more support for containers such as Docker and OpenShift.""The version control of the software is also an issue.""Deleting any service requires a lot of clean up, unlike Cloudera."

More Hortonworks Data Platform Cons →

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL.""I've experienced some incompatibilities when using the Delta Lake format.""The solution needs to include graphing capabilities. Including financial charts would help improve everything overall.""Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users.""There should be better integration with other solutions.""In the next update, we'd like to see better performance for small points of data. It is possible but there are better tools that are faster and cheaper.""SparkUI could have more advanced versions of the performance and the queries and all.""It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

More Spark SQL Cons →

Pricing and Cost Advice
  • "It is priced well and it is affordable"
  • "Currently, we are using the product in a sandbox environment, and there is no licensing. We might choose a licensing option once we get the results."
  • More Hortonworks Data Platform Pricing and Cost Advice →

  • "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
    Top Answer:Distributed computing, secure containerization, and governance capabilities are the most valuable features.
    Top Answer:I haven't done a price analysis specifically for HDP. However, when it was first introduced as Hadoop 2.0, there were a few use cases where the price was quite high. It was particularly expensive for… more »
    Top Answer:Since Cloudera acquired HDP, it's been bundled with CBH and HDP. However, the biggest challenge is cloud storage integration with Azure, GCP, and AWS. These platforms offer competitive storage… more »
    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
    6th
    out of 22 in Hadoop
    Views
    663
    Comparisons
    389
    Reviews
    5
    Average Words per Review
    354
    Rating
    8.0
    4th
    out of 22 in Hadoop
    Views
    1,569
    Comparisons
    1,005
    Reviews
    7
    Average Words per Review
    543
    Rating
    8.3
    Comparisons
    Also Known As
    Hortonworks, HDP
    Learn More
    Overview
    Hortonworks is a leading innovator in the industry, creating, distributing and supporting enterprise-ready open data platforms and modern data applications. Our mission is to manage the world's data. We have a single-minded focus on driving innovation in open source communities such as Apache Hadoop, NiFi, and Spark. We along with our 1600+ partners provide the expertise, training and services that allow our customers to unlock transformational value for their organizations across any line of business. Our connected data platforms powers modern data applications that deliver actionable intelligence from all data: data-in-motion and data-at-rest. We are Powering the Future of Data.
    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
    Mayo Clinic, Symantec, Progressive Insurance, Noble Energy, Cardinal Health, Rogers, Mercy, Neustar, TRUECar, T-Mobile
    UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions
    Top Industries
    REVIEWERS
    Comms Service Provider30%
    Manufacturing Company10%
    Government10%
    Financial Services Firm10%
    VISITORS READING REVIEWS
    Computer Software Company19%
    Financial Services Firm15%
    Government6%
    Outsourcing Company6%
    VISITORS READING REVIEWS
    Financial Services Firm21%
    Computer Software Company14%
    University8%
    Construction Company6%
    Company Size
    REVIEWERS
    Small Business25%
    Midsize Enterprise18%
    Large Enterprise57%
    VISITORS READING REVIEWS
    Small Business26%
    Midsize Enterprise13%
    Large Enterprise61%
    REVIEWERS
    Small Business36%
    Midsize Enterprise43%
    Large Enterprise21%
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise13%
    Large Enterprise74%
    Buyer's Guide
    Hortonworks Data Platform vs. Spark SQL
    March 2024
    Find out what your peers are saying about Hortonworks Data Platform vs. Spark SQL and other solutions. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Hortonworks Data Platform is ranked 6th in Hadoop with 25 reviews while Spark SQL is ranked 4th in Hadoop with 14 reviews. Hortonworks Data Platform is rated 8.0, while Spark SQL is rated 7.8. The top reviewer of Hortonworks Data Platform writes "Good for secure containerization, and governance capabilities ". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". Hortonworks Data Platform is most compared with Amazon EMR, Apache Spark, Cloudera DataFlow and HPE Ezmeral Data Fabric, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, SAP HANA, HPE Ezmeral Data Fabric and Netezza Analytics. See our Hortonworks Data Platform vs. Spark SQL report.

    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.