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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 March 2026, the mindshare of Apache Spark in the Hadoop category stands at 13.3%, down from 18.6% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.3%
Cloudera Distribution for Hadoop14.1%
HPE Data Fabric13.5%
Other59.1%
Hadoop

PeerResearch reports based on Apache Spark reviews

TypeTitleDate
CategoryHadoopMar 3, 2026Download
ProductReviews, tips, and advice from real usersMar 3, 2026Download
ComparisonApache Spark vs Cloudera Distribution for HadoopMar 3, 2026Download
ComparisonApache Spark vs Amazon EMRMar 3, 2026Download
ComparisonApache Spark vs HPE Data FabricMar 3, 2026Download
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Key learnings from peers
Last updated Mar 1, 2026

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 Enterprise14
Large Enterprise25
By reviewers
By visitors reading reviews
Company SizeCount
Small Business117
Midsize Enterprise35
Large Enterprise280
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
24%
Computer Software Company
8%
Manufacturing Company
7%
University
6%
Comms Service Provider
5%
Government
5%
Marketing Services Firm
5%
Insurance Company
4%
Retailer
4%
Educational Organization
4%
Healthcare Company
3%
Outsourcing Company
3%
Construction Company
2%
Media Company
2%
Non Profit
2%
Performing Arts
2%
Real Estate/Law Firm
2%
Recreational Facilities/Services Company
2%
Legal Firm
2%
Consumer Goods Company
1%
Transportation Company
1%
Pharma/Biotech Company
1%
Hospitality Company
1%
Renewables & Environment 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.
Consultant, Chief Engineer, Teamleiter at infoteam Software AG4.0I used Apache Spark for two years in an on-prem prototype; setup was straightforward and support was good. I liked its fast database access, transformation, and reliable data exchange/integration. Licensing seemed midrange, but the customer ultimately chose another technology.
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 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.
Senior Developer at Infosys3.5No summary available
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.