Apache SparkOpen-source multi-language engine for data engineering, data science and machine learning.
vs
TeradataTurn intelligence into autonomous action
Comparison · Data Warehouse · weekly run Nov 3
Apache Spark vs Teradata
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Teradata is the stronger AI pick in Data Warehouse: it has a visibility score of 5.7 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 Teradata if…
- You want the brand AI engines rank higher (5.7 vs 0.9)
- You need a self-hosted or on-premises option
- You need SOC 2 Type II or TX-RAMP or ISO/IEC 27001:2022 or IRAP PROTECTED or FedRAMP Ready at Moderate impact level on AWS coverage
- Suited to large, highly regulated enterprise environments
AI visibility side by side
Latest weekly run · Nov 3
| Metric | Apache Spark | Teradata |
|---|---|---|
| Visibility score | 0.9% | 5.7% |
| Change vs last week | New | New |
| Rank in Data Warehouse | #26 | #16 |
| Share of voice | 0.5% | 2.5% |
| Mentions this run | 3 | 14 |
| Positive in AI answers | — | 36% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Apache Spark 0pp · Teradata 0pp over 0 weekly runs.
Engine by engine
Teradata leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
Apache SparkTeradata
Head to head in AI answers
1 engine answers name both brands
0Apache Spark ranked higher
1Teradata ranked higher
| Prompt | Engine | Apache Spark | Teradata |
|---|---|---|---|
| How do companies store and query business data at scale without a dedicated tool? | AI Mode | #11 | #4 |
Features, pricing & fit
| Dimension | Apache Spark | Teradata |
|---|---|---|
| Deployment | Runs on single machines or on clusters: standalone, Hadoop YARN, Apache Mesos or Kubernetes. | Cloud, on-premises and hybrid |
| Compliance | — | SOC 2 Type II, TX-RAMP, ISO/IEC 27001:2022, IRAP PROTECTED, FedRAMP Ready at Moderate impact level on AWS |
| Founded | 2010 | 1979 |
| Headquarters | Wilmington, Delaware | San Diego, California |
| 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
Teradata
- Suited to large, highly regulated enterprise environments
- Mature SQL workload management and hybrid deployment options
- Strong for complex, governed enterprise workloads
- Good fit for modernizing existing Teradata environments
- High PeerSpot enterprise-warehouse rating
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Apache Spark vs Teradata”, https://slateindex.ai/compare/apache-spark-vs-teradata, data of Nov 3, 2026. Licence: CC BY 4.0.