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HPE Ezmeral Data Fabric vs Spark SQL comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

HPE Ezmeral Data Fabric
Ranking in Hadoop
5th
Average Rating
8.0
Number of Reviews
12
Ranking in other categories
No ranking in other categories
Spark SQL
Ranking in Hadoop
4th
Average Rating
7.8
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Hadoop category, the mindshare of HPE Ezmeral Data Fabric is 13.6%, up from 10.0% compared to the previous year. The mindshare of Spark SQL is 9.9%, down from 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Hadoop
 

Featured Reviews

Arnab Chatterjee - PeerSpot reviewer
Jan 18, 2023
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.
SurjitChoudhury - PeerSpot reviewer
Nov 23, 2023
Offers the flexibility to handle large-scale data processing
My experience with the initial setup of Spark SQL was relatively smooth. Understanding the system wasn't overly difficult because the data was structured in databases, and we could use notebooks for coding in Python or Java. Configuring networks and running scripts to load data into the database were routine tasks that didn't pose significant challenges. The flexibility to use different languages for coding and the ability to process data using key-value pairs in Python made the setup adaptable. Once we received the source data, processing it in SparkSQL involved writing scripts to create dimension and fact tables, which became a standard part of our workflow. Setting up Spark SQL was reasonably quick, but sometimes we face performance issues, especially during data loading into the SQL Server data warehouse. Sequencing notebooks for efficient job runs is crucial, and managing complex tasks with multiple notebooks requires careful tracking. Exploring ways to optimize this process could be beneficial. However, once you are familiar with the database architecture and project tools, understanding and adapting to the system become more straightforward.

Quotes from Members

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

Pros

"It is a stable solution...It is a scalable solution."
"HPE Ezmeral Data Fabric can be accessed from any namespace globally as you would access it from a machine using an NFS."
"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."
"I like the administration part."
"The model creation was very interesting, especially with the libraries provided by the platform."
"It is a stable solution."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"This solution is useful to leverage within a distributed ecosystem."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"Certain data sets that are very large are very difficult to process with Pandas and Python libraries. Spark SQL has helped us a lot with that."
 

Cons

"HPE Ezmeral Data Fabric is not compatible with third-party tools."
"Having the ability to extend the services provided by the platform to an API architecture, a micro-services architecture, could be very helpful."
"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."
"The deployment could be faster. I want more support for the data lake in the next release."
"Anything to improve the GUI would be helpful."
"There are many inconsistencies in syntax for the different querying tasks."
"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."
"This solution could be improved by adding monitoring and integration for the EMR."
"There should be better integration with other solutions."
"In the next release, maybe the visualization of some command-line features could be added."
"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."
 

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

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
15%
Manufacturing Company
7%
Retailer
7%
Financial Services Firm
25%
Computer Software Company
17%
Manufacturing Company
6%
University
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about HPE Ezmeral Data Fabric?
It is a stable solution...It is a scalable solution.
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 Fabric is not compatible with third-party tools. For example, HPE Ezmeral Data Fabri...
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 with the help of HPE Ezmeral Data Fabric. It is possible to use Spark engine with ...
What do you like most about Spark SQL?
Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline.
What is your experience regarding pricing and costs for Spark SQL?
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.
What needs improvement with Spark SQL?
In terms of improvement, the only thing that could be enhanced is the stability aspect of Spark SQL. There could be additional features that I haven't explored but the current solution for working ...
 

Also Known As

MapR, MapR Data Platform
No data available
 

Learn More

 

Overview

 

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
Find out what your peers are saying about HPE Ezmeral Data Fabric vs. Spark SQL and other solutions. Updated: October 2024.
814,649 professionals have used our research since 2012.