Apache SparkOpen-source multi-language engine for data engineering, data science and machine learning.
vs
AWSVisual low-code tool for building AWS-backed apps from Figma designs
Comparison · Data Warehouse · weekly run Nov 3
Apache Spark vs AWS
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
AWS is the stronger AI pick in Data Warehouse: it has a visibility score of 32.5 versus 0.9 for Apache Spark, and ranks ahead in 100% of the answers that name both.
Choose Apache Spark if…
- Strong distributed processing capabilities for massive workloads
- Distributes query execution across a cluster of commodity machines
Choose AWS if…
- You want the brand AI engines rank higher (32.5 vs 0.9)
- You need SOC 1 or SOC 2 or SOC 3 or ISO 27017 or ISO 27018 or ISO 27701 or PCI DSS Level 1 or FedRAMP or DoD SRG or ISO 9001 or ISO 27001 coverage
- Core federation and multi-account management included at no extra charge
- Enterprise-grade reliability and uptime on AWS infrastructure
AI visibility side by side
Latest weekly run · Nov 3
| Metric | Apache Spark | AWS |
|---|---|---|
| Visibility score | 0.9% | 32.5% |
| Change vs last week | New | New |
| Rank in Data Warehouse | #26 | #5 |
| Share of voice | 0.5% | 9.3% |
| Mentions this run | 3 | 52 |
| Positive in AI answers | — | 16% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Apache Spark 0pp · AWS 0pp over 0 weekly runs.
Engine by engine
AWS leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
Apache SparkAWS
Head to head in AI answers
2 engine answers name both brands
0Apache Spark ranked higher
2AWS ranked higher
| Prompt | Engine | Apache Spark | AWS |
|---|---|---|---|
| How do companies store and query business data at scale without a dedicated tool? | AI Mode | #11 | #8 |
| Compare the leading data warehouse platforms | ChatGPT | #8 | #6 |
Features, pricing & fit
| Dimension | Apache Spark | AWS |
|---|---|---|
| Deployment | Runs on single machines or on clusters: standalone, Hadoop YARN, Apache Mesos or Kubernetes. | — |
| Compliance | — | SOC 1, SOC 2, SOC 3, ISO 27017, ISO 27018, ISO 27701, PCI DSS Level 1, FedRAMP, DoD SRG, ISO 9001, ISO 27001 |
| Founded | 2010 | 2021 |
| Headquarters | Wilmington, Delaware | Seattle, Washington |
| Employees | 5,000+ | 5,000+ |
| Best for | Data engineering, data science and machine learning work on single machines or clusters. | — |
Pros and cons
Themes from the AI answers that mention each brand
Apache Spark
- Strong distributed processing capabilities for massive workloads
- Distributes query execution across a cluster of commodity machines
AWS
- Core federation and multi-account management included at no extra charge
- Enterprise-grade reliability and uptime on AWS infrastructure
- Pay-as-you-go, usage-based pricing
- Deep integration with the AWS ecosystem and AI tools
- Recognised by analysts such as Gartner and ISG
- Requires strong AWS engineering or technical expertise
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Apache Spark vs AWS”, https://slateindex.ai/compare/apache-spark-vs-aws, data of Nov 3, 2026. Licence: CC BY 4.0.