Spark SQL vs. Amazon Relational Database Service (RDS)
A side-by-side comparison based on G2 review data.
| Metric | Spark SQL | Amazon Relational Database Service (RDS) |
|---|---|---|
| Overall rating | 4.6 | 4.5 |
| Review count | 46 | 907 |
| Ease of use | 9.1 | 9.0 |
| Ease of setup | 9.0 | 9.0 |
| Quality of support | 8.6 | 8.8 |
| Meets requirements | 9.0 | 9.1 |
| NPS | 69.565217 | 66.164542 |
| Small Business | 26.1% | 28.8% |
| Mid-Market | 19.6% | 37.5% |
| Enterprise | 54.3% | 33.7% |
| Total page views | 35 | 646 |
| Total LLM citations | 20 | 2,389 |
Spark SQL
Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, Python and R.
Amazon Relational Database Service (RDS)
Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks such as hardware provisioning, database setup, patching and backups. It frees you up to focus on your apps so you can give them the better performance, availability, security, and compatibility they need. Amazon RDS is available on 6 familiar database instances: Amazon Aurora, MySQL, PostgreSQL, MariaDB, Oracle Database and SQL Server engines.
- Ease of Use
- Scalability
- Setup Ease
- Security
- Backup Services
- Reliability