Apache Spark vs Cloudera Distribution for Hadoop comparison

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Executive Summary

We performed a comparison between Apache Spark and Cloudera Distribution for Hadoop 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 Apache Spark vs. Cloudera Distribution for 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:
Pros
"I feel the streaming is its best feature.""The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations.""Spark can handle small to huge data and is suitable for any size of company.""Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark.""Features include machine learning, real time streaming, and data processing.""The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast.""ETL and streaming capabilities.""The data processing framework is good."

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"It has the best proxy, security, and support features compared to open-source products.""In terms of scalability, if you have enough hardware you can scale out. Scalability doesn't have any issues.""The product provides better data processing features than other tools.""Very good end-to-end security features.""Customer service and support were able to fix whatever the issue was.""We're now able to store large volumes of data through Cloudera Distribution for Hadoop. We're able to push large volumes of data to the platform, and that used to be a challenge, especially when storing a terabyte of information. This is the area where Cloudera Distribution for Hadoop improved the organization.""The product is completely secure.""The most valuable feature is Kubernetes."

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Cons
"The solution’s integration with other platforms should be improved.""I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it.""One limitation is that not all machine learning libraries and models support it.""We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.""There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance.""Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn.""Apache Spark's GUI and scalability could be improved.""It should support more programming languages."

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"The price of this solution could be lowered.""There are better solutions out there that have more features than this one.""Cloudera Distribution for Hadoop is not always completely stable in some cases, which can be a concern for big data solutions.""The areas of improvement depend on the scale of the project. For banking customers, security features and an essential budget for commercial licenses would be the top priority. Data regulation could be the most crucial for a project with extensive data or an extra use case.""The solution does not support multiple languages very well and this means users need to create work-arounds to implement some solutions.""While the deployed product is generally functional, there are instances where it presents difficulties.""We experienced many issues when we started working with Hadoop 3.0 in the Cloudera 6.0 version, so there is a lot of things that need to improve.""The dashboard could be improved."

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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."
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  • "When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive."
  • "The price could be better for the product."
  • "I haven't bought a license for this solution. I'm only using the Apache license version."
  • "Cloudera requires a license to use."
  • "Cloudera Distribution for Hadoop is expensive, with support costs involved."
  • "I wouldn't recommend CDH to others because of its high cost."
  • "The price is very high. The solution is expensive."
  • "The solution is expensive."
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    Questions from the Community
    Top Answer:The product’s most valuable features are lazy evaluation and workload distribution.
    Top Answer:They provide an open-source license for the on-premise version. However, we have to pay for the cloud version including data centers and virtual machines.
    Top Answer:They could improve the issues related to programming language for the platform.
    Top Answer:The product comes with an annual subscription, which is expensive. They are bundling technologies together. You have to pay an extra cost if you need the technology out of the base license.
    Ranking
    2nd
    out of 22 in Hadoop
    Views
    2,468
    Comparisons
    1,915
    Reviews
    20
    Average Words per Review
    387
    Rating
    8.6
    1st
    out of 22 in Hadoop
    Views
    3,103
    Comparisons
    2,377
    Reviews
    13
    Average Words per Review
    411
    Rating
    8.2
    Comparisons
    Learn More
    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

    Cloudera Distribution for Hadoop is the world's most complete, tested, and popular distribution of Apache Hadoop and related projects. CDH is 100% Apache-licensed open source and is the only Hadoop solution to offer unified batch processing, interactive SQL, and interactive search, and role-based access controls. More enterprises have downloaded CDH than all other such distributions combined.
    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
    37signals, Adconion,adgooroo, Aggregate Knowledge, AMD, Apollo Group, Blackberry, Box, BT, CSC
    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%
    REVIEWERS
    Financial Services Firm26%
    Computer Software Company22%
    Insurance Company15%
    Comms Service Provider11%
    VISITORS READING REVIEWS
    Financial Services Firm21%
    Computer Software Company16%
    Educational Organization9%
    Manufacturing Company8%
    Company Size
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    REVIEWERS
    Small Business27%
    Midsize Enterprise17%
    Large Enterprise56%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise9%
    Large Enterprise74%
    Buyer's Guide
    Apache Spark vs. Cloudera Distribution for Hadoop
    March 2024
    Find out what your peers are saying about Apache Spark vs. Cloudera Distribution for Hadoop and other solutions. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Hadoop with 58 reviews while Cloudera Distribution for Hadoop is ranked 1st in Hadoop with 46 reviews. Apache Spark is rated 8.4, while Cloudera Distribution for Hadoop is rated 7.8. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of Cloudera Distribution for Hadoop writes "Good end-to-end security features and we like that it's cloud independent". Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and AWS Lambda, whereas Cloudera Distribution for Hadoop is most compared with Amazon EMR, HPE Ezmeral Data Fabric, Cassandra, MongoDB and InfluxDB. See our Apache Spark vs. Cloudera Distribution for Hadoop report.

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    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.