Hadoop HDFS vs. Starburst
A side-by-side comparison based on G2 review data.
| Metric | Hadoop HDFS | Starburst |
|---|---|---|
| Overall rating | 4.4 | 4.3 |
| Review count | 141 | 127 |
| Ease of use | 7.9 | 8.9 |
| Ease of setup | 7.4 | 8.6 |
| Quality of support | 8.1 | 9.0 |
| Meets requirements | 8.8 | 9.0 |
| NPS | 54.615385 | 51.587302 |
| Small Business | 21.4% | 38.6% |
| Mid-Market | 22.9% | 23.6% |
| Enterprise | 55.7% | 37.8% |
| Total page views | 100 | 853 |
| Total LLM citations | 201 | 4,628 |
Hadoop HDFS
The Hadoop Distributed File System (HDFS) is a scalable and fault-tolerant file system designed to manage large datasets across clusters of commodity hardware. As a core component of the Apache Hadoop ecosystem, HDFS enables efficient storage and retrieval of vast amounts of data, making it ideal for big data applications. Key Features and Functionality: - Fault Tolerance: HDFS replicates data blocks across multiple nodes, ensuring data availability and resilience against hardware failures.
- Large Datasets
- Data Storage
- Data Security
- Data Processing
Starburst
Starburst is the data platform for analytics, applications, and AI, unifying data across clouds and on-premises to accelerate AI innovation. Organizations—from startups to Fortune 500 enterprises in 60+ countries—rely on Starburst for fast data access, seamless collaboration, and enterprise-grade governance on an open hybrid data lakehouse. Wherever data lives, Starburst unlocks its full potential, powering data and AI from development to deployment. By future-proofing data architecture, Starburst helps businesses fuel innovation with AI. Learn more at starburst.ai
- Fast Querying
- Query Efficiency
- Integrations
- Ease of Use
- Large Datasets
- Performance