Amazon EMR vs Apache Spark comparison

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2,198 views|1,882 comparisons
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2,468 views|1,915 comparisons
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Amazon EMR and Apache Spark 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 Amazon EMR vs. Apache Spark Report (Updated: March 2024).
765,234 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
"The project management is very streamlined.""We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot.""When we grade big jobs from on-prem to the cloud, we do it in EMR with Spark.""The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions.""In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance.""The solution is scalable.""One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR.""This is the best tool for hosts and it's really flexible and scalable."

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"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily.""The deployment of the product is easy.""Features include machine learning, real time streaming, and data processing.""There's a lot of functionality.""The product is useful for analytics.""Spark can handle small to huge data and is suitable for any size of company.""The most valuable feature of this solution is its capacity for processing large amounts of data.""We use Spark to process data from different data sources."

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Cons
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part.""The initial setup was time-consuming.""The product's features for storing data in static clusters could be better.""The dashboard management could be better. Right now, it's lacking a bit.""Amazon EMR can improve by adding some features, such as megastore services and HiveServer2. Additionally, the user interface could be better, similar to what Apache service provides, cross-platform services.""The legacy versions of the solution are not supported in the new versions.""The most complicated thing is configuring to the cluster and ensure it's running correctly.""The product must add some of the latest technologies to provide more flexibility to the users."

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"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate.""The product could improve the user interface and make it easier for new users.""The solution needs to optimize shuffling between workers.""Spark could be improved by adding support for other open-source storage layers than Delta Lake.""In data analysis, you need to take real-time data from different data sources. You need to process this in a subsecond, do the transformation in a subsecond, and all that.""Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use.""Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available.""We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

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Pricing and Cost Advice
  • "You don't need to pay for licensing on a yearly or monthly basis, you only pay for what you use, in terms of underlying instances."
  • "The cost of Amazon EMR is very high."
  • "The price of the solution is expensive."
  • "Amazon EMR's price is reasonable."
  • "There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."
  • "There is no need to pay extra for third-party software."
  • "Amazon EMR is not very expensive."
  • "The product is not cheap, but it is not expensive."
  • More Amazon EMR 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."
  • More Apache Spark Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Amazon EMR is a good solution that can be used to manage big data.
    Top Answer:As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data.
    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.
    Ranking
    3rd
    out of 22 in Hadoop
    Views
    2,198
    Comparisons
    1,882
    Reviews
    12
    Average Words per Review
    346
    Rating
    7.8
    2nd
    out of 22 in Hadoop
    Views
    2,468
    Comparisons
    1,915
    Reviews
    20
    Average Words per Review
    387
    Rating
    8.6
    Comparisons
    Also Known As
    Amazon Elastic MapReduce
    Learn More
    Overview
    Amazon Elastic MapReduce (Amazon EMR) is a web service that makes it easy to quickly and cost-effectively process vast amounts of data. Amazon EMR simplifies big data processing, providing a managed Hadoop framework that makes it easy, fast, and cost-effective for you to distribute and process vast amounts of your data across dynamically scalable Amazon EC2 instances.

    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

    Sample Customers
    Yelp
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    Top Industries
    REVIEWERS
    Computer Software Company27%
    Wholesaler/Distributor18%
    Media Company18%
    Comms Service Provider9%
    VISITORS READING REVIEWS
    Financial Services Firm23%
    Computer Software Company13%
    Manufacturing Company8%
    Educational Organization6%
    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%
    Company Size
    REVIEWERS
    Small Business26%
    Midsize Enterprise26%
    Large Enterprise47%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise72%
    REVIEWERS
    Small Business40%
    Midsize Enterprise19%
    Large Enterprise40%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    Buyer's Guide
    Amazon EMR vs. Apache Spark
    March 2024
    Find out what your peers are saying about Amazon EMR vs. Apache Spark and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    Amazon EMR is ranked 3rd in Hadoop with 20 reviews while Apache Spark is ranked 2nd in Hadoop with 58 reviews. Amazon EMR is rated 7.8, while Apache Spark is rated 8.4. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". Amazon EMR is most compared with Cloudera Distribution for Hadoop, Snowflake, Amazon Redshift, Azure Data Factory and Microsoft Azure Synapse Analytics, whereas Apache Spark is most compared with Spring Boot, AWS Batch, Spark SQL, SAP HANA and AWS Fargate. See our Amazon EMR vs. Apache Spark 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.