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Apache Hadoop Reviews

Vendor: Apache
3.9 out of 5

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Featured Apache Hadoop reviews

Apache Hadoop mindshare

As of August 2025, the mindshare of Apache Hadoop in the Data Warehouse category stands at 4.8%, down from 4.9% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Data Warehouse Market Share Distribution
ProductMarket Share (%)
Apache Hadoop4.8%
Oracle Exadata16.4%
Teradata15.2%
Other63.6%
Data Warehouse

PeerResearch reports based on Apache Hadoop reviews

TypeTitleDate
CategoryData WarehouseAug 27, 2025Download
ProductReviews, tips, and advice from real usersAug 27, 2025Download
ComparisonApache Hadoop vs SnowflakeAug 27, 2025Download
ComparisonApache Hadoop vs Oracle ExadataAug 27, 2025Download
ComparisonApache Hadoop vs TeradataAug 27, 2025Download
Suggested products
TitleRatingMindshareRecommending
Teradata4.115.2%87%76 interviewsAdd to research
Snowflake4.214.0%97%101 interviewsAdd to research
 
 
Key learnings from peers

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 Business11
Midsize Enterprise7
Large Enterprise15
By reviewers
By visitors reading reviews
Company SizeCount
Small Business61
Midsize Enterprise46
Large Enterprise241
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
35%
Computer Software Company
11%
University
6%
Energy/Utilities Company
5%
Government
4%
Manufacturing Company
3%
Comms Service Provider
3%
Performing Arts
3%
Transportation Company
3%
Media Company
3%
Educational Organization
3%
Non Profit
3%
Real Estate/Law Firm
2%
Healthcare Company
2%
Retailer
2%
Wholesaler/Distributor
1%
Construction Company
1%
Hospitality Company
1%
Insurance Company
1%
Outsourcing Company
1%
Recreational Facilities/Services Company
1%
Logistics Company
1%
Pharma/Biotech Company
1%
Legal Firm
1%
Renewables & Environment Company
1%
 
Apache Hadoop Reviews Summary
Author infoRatingReview Summary
Financial Advisor at a financial services firm with 10,001+ employees4.0We had a limited on-premises deployment of Apache Hadoop which scaled well and was reliable, but maintaining it was challenging due to a lack of resources and expertise after the original team left. We prefer solutions with structured support.
Software developer at Fiserv4.0I use Apache Hadoop in my company for its efficient analytical processing and organized data distribution. However, it struggles with incremental data processing and has high licensing costs. Setup and technical support need improvement for better user experience and resolution times.
Senior Assosiate Consultant at Applied Materials3.0I use Apache Hadoop for data storage and report generation, appreciating its open-source nature, ability to handle large data volumes, and effectiveness in data processing and storage. However, limited support requires deeper knowledge and improvisation.
Head Of Data Governance at Alibaba Group4.0We use the Hadoop File System for big data due to its open-source nature and cost-effectiveness. However, dealing with data skewness requires custom solutions, unlike Spark, which is more efficient and faster due to in-memory processing.
IT Support Specialist at Convergys Corporation4.5We used Apache Hadoop mainly for data analysis and storage, benefiting from its low cost, open-source nature, and efficient performance on commodity hardware. While its flexibility and resilience are advantageous, improved security measures would enhance its capabilities.
Database Administrator at Lacoste4.5I am working on migrating a customer's data warehouse from Oracle to Hadoop, specifically considering Cloudera, due to its data warehouse capabilities and scalability. The integration of reporting tools like Power BI with Hadoop poses some challenges.
Manager at Robi Axiata Limited3.5No summary available
Head of Data at a energy/utilities company with 51-200 employees4.5Apache Hadoop's distributed computing capability efficiently accelerates data processing by distributing tasks across multiple nodes. While it offers cost savings on compute resources through optimization, the availability of comprehensive training materials could be improved to enhance onboarding and skill development.