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Apache Spark Reviews

Vendor: Apache
4.2 out of 5
Badge Ranked 1

What is Apache Spark?

Featured Apache Spark reviews

Apache Spark mindshare

As of December 2025, the mindshare of Apache Spark in the Hadoop category stands at 15.5%, down from 17.8% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark15.5%
Cloudera Distribution for Hadoop16.5%
HPE Data Fabric15.2%
Other52.8%
Hadoop

PeerResearch reports based on Apache Spark reviews

TypeTitleDate
CategoryHadoopDec 29, 2025Download
ProductReviews, tips, and advice from real usersDec 29, 2025Download
ComparisonApache Spark vs Cloudera Distribution for HadoopDec 29, 2025Download
ComparisonApache Spark vs HPE Data FabricDec 29, 2025Download
ComparisonApache Spark vs Amazon EMRDec 29, 2025Download
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Key learnings from peers
Last updated Nov 21, 2025

Valuable Features

Room for Improvement

ROI

Pricing

Popular Use Cases

Service and Support

Deployment

Scalability

Stability

Review data by company size

By reviewers
Company SizeCount
Small Business25
Midsize Enterprise13
Large Enterprise25
By reviewers
By visitors reading reviews
Company SizeCount
Small Business112
Midsize Enterprise37
Large Enterprise335
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
11%
Manufacturing Company
7%
Comms Service Provider
6%
University
6%
Government
5%
Insurance Company
4%
Retailer
4%
Healthcare Company
4%
Educational Organization
4%
Outsourcing Company
2%
Media Company
2%
Construction Company
2%
Real Estate/Law Firm
2%
Non Profit
2%
Performing Arts
2%
Legal Firm
1%
Recreational Facilities/Services Company
1%
Transportation Company
1%
Marketing Services Firm
1%
Pharma/Biotech Company
1%
Renewables & Environment Company
1%
Consumer Goods Company
1%
Hospitality Company
1%
Energy/Utilities Company
1%
 
Apache Spark Reviews Summary
Author infoRatingReview Summary
Data Architect at Devtech4.5I’ve used Apache Spark for four years, mainly for data integration and access. Its in-memory processing and open-source flexibility suit my needs, despite some stability issues. I prefer it over commercial tools like Informatica due to cost and adaptability.
Data Engineer at a tech company with 10,001+ employees5.0I use Apache Spark for real-time data processing and transformation across multiple sources like CRM and Siebel. It's reliable, fast, and improves our decision-making, though I see future needs for better integration with emerging cloud solutions.
Senior Developer at Infosys3.5No summary available
Senior Software Architect at USEReady4.0No summary available
Data Scientist at a financial services firm with 10,001+ employees4.5I primarily use Apache Spark for data processing tasks involving large datasets, appreciating its ease of use and portability. While it's efficient for both small and large datasets, the lack of support for geospatial data is a limitation.
Data engineer at Cocos pt4.5We use Apache Spark primarily for Spark SQL and occasionally Spark Streaming, processing data from sources like SAP and Azure Data Warehouse. Its in-memory processing significantly outperforms Hadoop, offering faster data handling and enhanced query optimization.
Head of Data at a energy/utilities company with 51-200 employees4.0Apache Spark significantly reduced operational costs by 50% and although it supports parallel processing, it needs improvements in scalability and user-friendliness. Working with datasets isn't as straightforward as with Pandas, though it's flexible and functional.
Director Product Development at Mycom Osi4.0In my company, we use Apache Spark for topology engines and chains. While it is a valuable tool, finding skilled developers is challenging. The deployment phase sometimes requires manual interventions, especially with large datasets, indicating areas for improvement.