HPE Data Fabric vs Spark SQL comparison

Why PeerSpot?

Hewlett Packard Enterprise and Apache are both solutions in the Hadoop category. Hewlett Packard Enterprise is ranked #5, while Apache is ranked #4 with an average rating of 8.0. Hewlett Packard Enterprise holds a 9.2% mindshare in H, compared to Apache’s 5.3% mindshare. Additionally, 100% of Hewlett Packard Enterprise users are willing to recommend the solution, compared to 86% of Apache users who would recommend it.

HPE Data Fabric

Read 12 HPE Data Fabric reviews

2,863 Views
2,376 Comparison Views

100% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

1,824 Views
1,623 Comparison Views

86% willing to recommend

HPE Data Fabric

Spark SQL

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between HPE Data Fabric 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 HPE Data Fabric vs. Spark SQL Report (Updated: November 2026).

Buyer's Guide

HPE Data Fabric vs. Spark SQL

November 2026

Download the complete report

Helped 921,523 peers since 2012

Review summaries and opinions

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

Categories and Ranking

HPE Data Fabric

Ranking in Hadoop

5th

Average Rating

8.0

Reviews Sentiment

6.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Spark SQL

Ranking in Hadoop

4th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Hadoop category, the mindshare of HPE Data Fabric is 9.2%, down from 14.5% compared to the previous year. The mindshare of Spark SQL is 5.3%, down from 7.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Mindshare Distribution
Product	Mindshare (%)
Spark SQL	5.3%
HPE Data Fabric	9.2%
Other	85.5%

Hadoop

Featured Reviews

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.

Read full review

Kemal Duman

Team Lead, Data Engineering at Nesine.com

Data pipelines have run faster and support flexible batch and streaming transformations

We do not have any performance problems, but we do have some resource problems. Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink. Resource management in Spark SQL should be better. It consumes more resources, which is normal. The main reason we switched from Spark is memory and CPU consumption. The major reason is the resource problem because the number of streaming jobs has been increasing in our company. That is why we considered resource management as a priority. Because of the resource consumption, I would say the development of Spark SQL is better. For development purposes, it is a top product and not difficult to work with, but resources are the major problem. We changed to Flink regardless of development time. Development time is less in Spark compared with Flink.

Read full review

Quotes from Members

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

Pros

"Initial setup is rather straightforward thanks to detailed documentation covering all the bases."

"Our customer purchased a paid support service and so far MapR has addressed our issues well."

"The fact that the heavy computation is required on Big Data can be distributed across many nodes in a cluster, makes this solution a winner."

"MapR is a great distribution, although I have limited experience with other distributors."

"Outside of human error, MapR is probably the most stable of the major releases."

"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."

"My first choice is MapR, as it is more adaptable to different contexts, and it could be customized in some way to fit the different needs, and this is my first choice and my first advice to people who ask me about this particular platform."

"I highly recommend MapR."

More HPE Data Fabric pros

"Overall the solution is excellent."

"We use it to gather all the transaction data."

"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."

"Data validation and ease of use are the most valuable features."

"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."

"The performance is one of the most important features, and it has an API to process the data in a functional manner."

"I find the Thrift connection valuable."

"The scalability of the solution is good."

More Spark SQL pros

Cons

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

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

"I'd say we've had issues with pricing."

"The product is not user-friendly."

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

"Installations and setups are still a bit cryptic and can be improved."

"One weakness for MapR is the Kerberos support."

"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."

More HPE Data Fabric cons

"This solution could be improved by adding monitoring and integration for the EMR."

"I've experienced some incompatibilities when using the Delta Lake format."

"It takes a bit of time to get used to using this solution versus Pandas as it has a steep learning curve."

"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."

"Spark SQL consumes so many resources that we migrated our streaming job from Spark to Apache Flink."

"SparkUI could have more advanced versions of the performance and the queries and all."

"The initial setup is a bit complex."

"There should be better integration with other solutions."

More Spark SQL cons

Pricing and Cost Advice

"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."

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

"There is no license or subscription for this solution."

"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."

"The solution is open-sourced and free."

"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."

"The solution is bundled with Palantir Foundry at no extra charge."

"We use the open-source version, so we do not have direct support from Apache."

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Top Industries

By visitors reading reviews

Construction Company

17%

Financial Services Firm

15%

Comms Service Provider

10%

Healthcare Company

Financial Services Firm

15%

Comms Service Provider

11%

Manufacturing Company

11%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	6
Large Enterprise	4

Questions from the Community

Ask a question

Earn 20 points

What needs improvement with Spark SQL?

See all answers

What is your primary use case for Spark SQL?

Spark SQL has been in our stack for less than one year, though some of our colleagues are using it. It is a useful product for transformation jobs. We generally use Spark SQL for batch processing. ...

See all answers

What advice do you have for others considering Spark SQL?

Regarding the Catalyst query optimizer, I think we are using it. We were using it in the past, but I am not certain if we use it now. We used it a long time ago. I rate my experience with Spark SQL...

See all answers

Comparisons

Cloudera Distribution for Hadoop vs HPE Data Fabric

Compared 16% of the time

IBM Spectrum Computing vs HPE Data Fabric

Compared 11% of the time

Amazon EMR vs HPE Data Fabric

Compared 11% of the time

IBM Analytics Engine vs HPE Data Fabric

Compared 11% of the time

SAP HANA vs HPE Data Fabric

Compared 4% of the time

More HPE Data Fabric Competitors

Amazon EMR vs Spark SQL

Compared 21% of the time

Apache Spark vs Spark SQL

Compared 21% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 18% of the time

IBM Netezza Performance Server vs Spark SQL

Compared 8% of the time

IBM Analytics Engine vs Spark SQL

Compared 6% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

HPE Data Fabric

October 2026

Download HPE Data Fabric product report

Buyer's Guide

Spark SQL

October 2026

Download Spark SQL product report

Also Known As

MapR, MapR Data Platform

No data available

Overview

HPE Data Fabric delivers robust data management with features like multi-tenancy, security, and ease of configuration. It supports high performance and unified analytics, making it a reliable choice for organizations looking to manage extensive data efficiently.

HPE Data Fabric provides a comprehensive data management platform with clustered node distribution and no single point of failure, ensuring high availability. Its compatibility with MapR-DB and NFS functionality allows integration with existing systems. Although there are challenges with third-party tool compatibility and upgrades, it supports big data initiatives by acting as both a database and messaging layer. Users benefit from bundled ecosystem support and simplified administration, enhancing usability across multiple teams and locations.

What features make HPE Data Fabric valuable?

Multi-tenancy: Supports multiple users or tenants securely.
Clustered Node Distribution: Distributes data across nodes for reliability.
POSIX Compliance: Compatibility with POSIX standards for ease of integration.
NFS Functionality: Facilitates network-based file sharing.
Unified Analytics: Provides integrated data analysis capabilities.

Why consider HPE Data Fabric in reviews?

Reliability: No single point of failure, ensuring consistent performance.
Cost Efficiency: Offers cost-effective data management solutions.
High Availability: Provides resilient storage and service availability.
Ease of Configuration: Streamlines administrative tasks with UI-driven tools.

Organizations in sectors such as finance, healthcare, and logistics use HPE Data Fabric to manage large volumes of data efficiently. Its role in supporting distributed processing and acting as a NoSQL storage solution enables these industries to leverage big data for enhanced operational insights and decision-making capabilities. The inclusion of AI tools further expands its utility, facilitating advanced data environments that are cost-effective and scalable for growing organizational demands.

Hewlett Packard Enterprise

Spark SQL leverages SQL capabilities to process large datasets, offering high performance, seamless integration with Spark programs, and the ability to run parallel queries. It supports Hive interoperability and facilitates data transformation with DataFrames and Datasets.

Spark SQL enables efficient data engineering, transformation, and analytics for organizations dealing with large-scale data processing. It supports big data queries, builds data pipelines and warehouses, and interfaces with various databases, especially in distributed settings such as Hadoop and Azure. Users employ Spark SQL to establish business logic in Jupyter notebooks and facilitate data loading into SQL Server, enabling analytics with tools like Power BI. The documentation and flexibility to manage extensive data processing are valued by users, although a steep learning curve and documentation clarity are noted challenges. Enhancements for data visualization, GUI, and resource management alongside better integration with tools like Tableau are recommended.

What are the key features of Spark SQL?

Query Language: Supports complex SQL queries for effective large dataset processing.
Seamless Integration: Easily integrates into Spark programs, boosting performance and speed.
Parallel Queries: Capable of running numerous queries in parallel to enhance processing efficiency.
Interoperability with Hive: Facilitates interaction within distributed data ecosystems efficiently.
Data Transformation: Utilizes DataFrames and Datasets for flexible data manipulation.

What benefits or ROI should users consider in reviews?

Performance: Offers high-speed data processing for demanding workloads.
Scalability: Efficiently handles increasing data volumes and processing demands.
Ease of Use: Facilitates simpler SQL-based data management operations.
Flexibility: Adapts to various data sources and formats for broad usability.

In industries, Spark SQL is a critical part of data engineering, transformation, and analytics. It empowers organizations to manage big data processing and analytics in sectors like finance, healthcare, and telecommunications. By enabling seamless data pipeline creation, it supports real-time business decision-making processes and data-driven strategies across sectors.

Apache

Sample Customers

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

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, Hitachi Solutions

Buyer's Guide

HPE Data Fabric vs. Spark SQL

November 2026

Free Report: HPE Data Fabric vs. Spark SQL

Find out what your peers are saying about HPE Data Fabric vs. Spark SQL and other solutions. Updated: November 2026.

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