Amazon EMR vs Apache Spark vs HPE Ezmeral Data Fabric comparison

Amazon Web Services (AWS) and Apache are both solutions in the Hadoop category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while Apache is ranked #1 with an average rating of 8.6. Amazon Web Services (AWS) holds a 13.9% mindshare in H, compared to Apache’s 17.8% mindshare. Additionally, 86% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Amazon EMR

Read 23 Amazon EMR reviews

1,035 Views
872 Comparison Views

86% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

1,404 Views
1,090 Comparison Views

90% willing to recommend

HPE Ezmeral Data Fabric

Read 12 HPE Ezmeral Data Fabric reviews

1,225 Views
640 Comparison Views

100% willing to recommend

Amazon EMR

Apache Spark

HPE Ezmeral Data Fabric

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Executive Summary

We performed a comparison between Amazon EMR, Apache Spark, and HPE Ezmeral Data Fabric based on real PeerSpot user reviews.

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop.

To learn more, read our detailed Hadoop Report (Updated: May 2025).

Buyer's Guide

Hadoop

May 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Mindshare comparison

As of May 2025, in the Hadoop category, the mindshare of Amazon EMR is 13.9%, down from 17.2% compared to the previous year. The mindshare of Apache Spark is 17.8%, down from 21.4% compared to the previous year. The mindshare of HPE Ezmeral Data Fabric is 15.2%, up from 11.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop

Featured Reviews

Prashant Singh

Vice President -Product Management at Paytm

Seamless data integration enhances reporting efficiency and an easy setup

Amazon EMR has multiple connectors that can connect to various data sources. The service charges are based on processing only, depending on the resources used, which can help save money. It is easy to integrate with other services for storage, allowing data to be shifted to cheaper storage based on usage.

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Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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Arnab Chatterjee

Regional Head of Data and Application Platform at a financial services firm with 10,001+ employees

It's flexible and easily accessible across multiple locations, but the upgrade process is complicated

Upgrading Ezmeral to a new version is a pain. They're trying to make the solution more container-friendly, so I think they're going in the right direction. The only problem we've had in the past was the upgrades. The process isn't smooth due to how the Red Hat operating system upgrades currently work. They're transforming their host stack to increase cloud readiness and edge compute capability. HPE is transitioning from a standard data-driven approach to one powered by AI analytics. That's something they have released very recently. I haven't tried that, but it will probably make things easier. The ability to adapt Ezmeral to the public cloud is probably missing. I've heard that they're getting leaner. However, it doesn't have a clear managed services offering for you if you want to deploy this stack on the cloud. That's a problem. This probably won't meet your needs if you require consistency across on-prem and the cloud. It's not Ezmeral's fault. None of the products would fit the bill. Cloud offerings are biased towards their own implementation. It's a general issue on most big data platforms. They're already working towards that, but it hasn't been released.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The initial setup is straightforward."

"The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions."

"The solution helps us manage huge volumes of data."

"This is the best tool for hosts and it's really flexible and scalable."

"Amazon EMR is a good solution that can be used to manage big data."

"The project management is very streamlined."

"The solution is scalable."

"It allows users to access the data through a web interface."

More Amazon EMR pros

"I feel the streaming is its best feature."

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."

"It's easy to prepare parallelism in Spark, run the solution with specific parameters, and get good performance."

"With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware."

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"The most valuable feature of Apache Spark is its ease of use."

"The main feature that we find valuable is that it is very fast."

"The product’s most valuable features are lazy evaluation and workload distribution."

More Apache Spark pros

"The model creation was very interesting, especially with the libraries provided by the platform."

"I like the administration part."

"It is a stable solution...It is a scalable solution."

"My customers find the product cheaper compared to other solutions. The previous solution that we used did not have unified analytics like the runtime or the analog."

"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."

Cons

"Spark jobs take longer on Amazon EMR compared to previous experiences."

"Modules and strategies should be better handled and notified early in advance."

"The most complicated thing is configuring to the cluster and ensure it's running correctly."

"There is no need to pay extra for third-party software."

"The solution can become expensive if you are not careful."

"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 product's features for storing data in static clusters could be better."

"The problem for us is it starts very slow."

More Amazon EMR cons

"It requires overcoming a significant learning curve due to its robust and feature-rich nature."

"The Spark solution could improve in scheduling tasks and managing dependencies."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"The logging for the observability platform could be better."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

"We are building our own queries on Spark, and it can be improved in terms of query handling."

"When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources."

"The product could improve the user interface and make it easier for new users."

More Apache Spark cons

"Upgrading Ezmeral to a new version is a pain. They're trying to make the solution more container-friendly, so I think they're going in the right direction. The only problem we've had in the past was the upgrades. The process isn't smooth due to how the Red Hat operating system upgrades currently work."

"The deployment could be faster. I want more support for the data lake in the next release."

"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."

"HPE Ezmeral Data Fabric is not compatible with third-party tools."

"The product is not user-friendly."

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."

"There is no need to pay extra for third-party software."

"I rate the tool's pricing a five out of ten. It can be expensive since it's a managed service, and if you are not careful, you can run into unexpected charges. You can make a mistake that costs you tens of thousands of dollars. That's happened to us twice, so I'm sensitive to it. We're still trying to work on that. Our smallest client probably spends a hundred thousand dollars yearly on licensing, while our largest is well over a million."

"Amazon EMR's price is reasonable."

"The product is not cheap, but it is not expensive."

"The price of the solution is expensive."

"There is a small fee for the EMR system, but major cost components are the underlying infrastructure resources which we actually use."

"The cost of Amazon EMR is very high."

More Amazon EMR pricing and cost advice

"Apache Spark is an open-source tool."

"We are using the free version of the solution."

"The product is expensive, considering the setup."

"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."

"They provide an open-source license for the on-premise version."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

More Apache Spark pricing and cost advice

"There is a need for my company to pay for the licensing costs of the solution."

"The tool's price is cheap and based on a usage basis. The solution's licensing costs are yearly and there are no extra costs."

"HPE is flexible with you if you are an existing customer. They offer different models that might be beneficial for your organization. It all depends on how you negotiate."

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

14%

Educational Organization

Manufacturing Company

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Financial Services Firm

20%

Computer Software Company

16%

Retailer

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Amazon EMR?

Amazon EMR is a good solution that can be used to manage big data.

See all answers

What is your experience regarding pricing and costs for Amazon EMR?

Compared to others, Amazon seems efficient and is considered good for Big Data workloads. Costs are involved based on...

See all answers

What needs improvement with Amazon EMR?

There is room for improvement with respect to retries, handling the volume of data on S3 ( /products/amazon-s3-review...

See all answers

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requir...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential ...

See all answers

What do you like most about HPE Ezmeral Data Fabric?

It is a stable solution...It is a scalable solution.

See all answers

What needs improvement with HPE Ezmeral Data Fabric?

There are some drawbacks in HPE Ezmeral Data Fabric when it comes to the interoperability part. HPE Ezmeral Data Fabr...

See all answers

What is your primary use case for HPE Ezmeral Data Fabric?

The main purpose of HPE Ezmeral Data Fabric for me is that it acts as a database. In my company, we store our data wi...

See all answers

Comparisons

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Compared 6% of the time

Teradata vs Amazon EMR

Compared 3% of the time

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Spring Boot vs Apache Spark

Compared 27% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Jakarta EE vs Apache Spark

Compared 3% of the time

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Cloudera Distribution for Hadoop vs HPE Ezmeral Data Fabric

Compared 59% of the time

Spark SQL vs HPE Ezmeral Data Fabric

Compared 9% of the time

IBM Analytics Engine vs HPE Ezmeral Data Fabric

Compared 7% of the time

Cloudera Data Platform vs HPE Ezmeral Data Fabric

Compared 7% of the time

MongoDB vs HPE Ezmeral Data Fabric

Compared 6% of the time

More HPE Ezmeral Data Fabric Competitors

Product Reports

Buyer's Guide

Amazon EMR

May 2025

Download Amazon EMR product report

Buyer's Guide

Apache Spark

May 2025

Download Apache Spark product report

Buyer's Guide

HPE Ezmeral Data Fabric

April 2025

Download HPE Ezmeral Data Fabric product report

Also Known As

Amazon Elastic MapReduce

No data available

MapR, MapR Data Platform

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.

Amazon Web Services (AWS)

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

Apache

Forward-leaning companies win market share because they leverage data more effectively than their competitors. Unlock the potential of your data assets with HPE Ezmeral Data Fabric (formerly MapR Data Platform). Empower your data science, analytics, and business teams by simplifying data management on a globally distributed scale. All with enterprise-grade reliability, security, and performance.

Hewlett Packard Enterprise

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

Valence Health, Goodgame Studios, Pico, Terbium Labs, sovrn, Harte Hanks, Quantium, Razorsight, Novartis, Experian, Dentsu ix, Pontis Transitions, DataSong, Return Path, RAPP, HP

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: May 2025.

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