Hadoop HDFS vs. Databricks
A side-by-side comparison based on G2 review data.
| Metric | Hadoop HDFS | Databricks |
|---|---|---|
| Overall rating | 4.4 | 4.6 |
| Review count | 141 | 1,388 |
| Ease of use | 7.9 | 8.9 |
| Ease of setup | 7.4 | 8.7 |
| Quality of support | 8.1 | 8.9 |
| Meets requirements | 8.8 | 9.1 |
| NPS | 54.615385 | 77.851361 |
| Small Business | 21.4% | 15.1% |
| Mid-Market | 22.9% | 37.6% |
| Enterprise | 55.7% | 47.2% |
| Total page views | 100 | 16,340 |
| Total LLM citations | 201 | 14,031 |
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
Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog.
- Features
- Ease of Use
- Integrations
- Collaboration
- Analytics
- Easy Integrations