HPE Data Fabric vs Spark SQL comparison

Hewlett Packard Enterprise and Apache are both solutions in the Hadoop category. Hewlett Packard Enterprise is ranked #4, while Apache is ranked #5 with an average rating of 8.0. Hewlett Packard Enterprise holds a 9.8% mindshare in H, compared to Apache’s 5.1% 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

2,389 Views
1,983 Comparison Views

100% willing to recommend

Spark SQL

Read 15 Spark SQL reviews

1,471 Views
1,309 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: August 2026).

Buyer's Guide

HPE Data Fabric vs. Spark SQL

August 2026

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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:

Categories and Ranking

HPE Data Fabric

Ranking in Hadoop

4th

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

5th

Average Rating

7.8

Reviews Sentiment

7.6

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Hadoop category, the mindshare of HPE Data Fabric is 9.8%, down from 14.8% compared to the previous year. The mindshare of Spark SQL is 5.1%, down from 10.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Hadoop Mindshare Distribution
Product	Mindshare (%)
HPE Data Fabric	9.8%
Spark SQL	5.1%
Other	85.1%

Hadoop

Featured Reviews

Hamid M. Hamid

Data architect at Banking Sector

A stable and scalable tool that serves as a great database

The initial setup of HPE Ezmeral Data Fabric is easy. I am not sure how long it took to deploy HPE Ezmeral Data Fabric, but I haven't heard about any disadvantages when it comes to the time taken for the deployment. I remember that one of our company's clients who had purchased the product never mentioned the product's setup phase being complex. One of the drawbacks with HPE Ezmeral Data Fabric stems from the fact that the product's upgrade was not straightforward, and it was a complex process since one of the teams in my company who deals with the tool found the upgrade part to be tough. The solution is deployed on an on-premises model. My company has two dedicated staff members to look after the deployment and maintenance phases of HPE Ezmeral Data Fabric.

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

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

"I like the administration part."

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

"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."

"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."

"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."

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

More HPE Data Fabric pros

"The scalability of the solution is good."

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

"It is a stable solution."

"The solution is easy to understand if you have basic knowledge of SQL commands."

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

"This solution is useful to leverage within a distributed ecosystem."

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

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

More Spark SQL pros

Cons

"The interface part, what I'm calling the integration part, could be improved."

"It would be nice to have new developments in the Apache space (Spark, Storm, etc.)."

"One weakness for MapR is the Kerberos support."

"The product is not user-friendly."

"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."

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

"The UI for administration still has a lot of manual work to set up the cluster and get it running."

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

More HPE Data Fabric cons

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

"It would be useful if Spark SQL integrated with some data visualization tools."

"In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL."

"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."

"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."

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

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

"There should be better integration with other solutions."

More Spark SQL cons

Pricing and Cost Advice

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

"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."

"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."

"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."

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

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

"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."

"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

18%

Financial Services Firm

17%

Healthcare Company

10%

Comms Service Provider

Financial Services Firm

22%

University

11%

Comms Service Provider

Healthcare Company

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

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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 15% of the time

IBM Spectrum Computing vs HPE Data Fabric

Compared 12% 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 23% of the time

Apache Spark vs Spark SQL

Compared 21% of the time

IBM Db2 Big SQL vs Spark SQL

Compared 19% of the time

IBM Netezza Performance Server vs Spark SQL

Compared 9% of the time

IBM Analytics Engine vs Spark SQL

Compared 7% of the time

More Spark SQL Competitors

Product Reports

Buyer's Guide

HPE Data Fabric

July 2026

Download HPE Data Fabric product report

Buyer's Guide

Spark SQL

July 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

August 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: August 2026.

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