Spark SQL vs. Tiger Data
A side-by-side comparison based on G2 review data.
| Metric | Spark SQL | Tiger Data |
|---|---|---|
| Overall rating | 4.6 | 4.6 |
| Review count | 46 | 32 |
| Ease of use | 9.1 | 9.1 |
| Ease of setup | 9.0 | 8.7 |
| Quality of support | 8.6 | 9.6 |
| Meets requirements | 9.0 | 9.3 |
| NPS | 69.565217 | 68.75 |
| Small Business | 26.1% | 78.1% |
| Mid-Market | 19.6% | 18.8% |
| Enterprise | 54.3% | 3.1% |
| Total page views | 35 | 481 |
| Total LLM citations | 20 | 5,519 |
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.
Tiger Data
Tiger Data, from the creators of TimescaleDB, is the #1 Postgres time-series database for developers, devices and agents. Keep sensor, on-chain, and customer data fresh while retaining years of history, all queryable in standard SQL. For IoT, Web3, and AI. Why teams choose Tiger Data: - Trusted by thousands of developers. 3M+ active databases, 2k+ customers - Up to 95% Compression. Keep years of history online at a fraction of the cost. - Production-ready without the operational pain. Multi-AZ HA, PITR, cross-region backups, SOC 2/HIPAA/GDPR, deep observability. - Scale effortlessly.
- Ease of Use
- Easy Setup
- Setup Ease
- Performance
- Analytics
- Speed