Apache SparkOpen-source multi-language engine for data engineering, data science and machine learning.
vs
dbtDeliver trusted data with dbt
Comparison · Data Warehouse · weekly run Nov 3
Apache Spark vs dbt
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
dbt is the stronger AI pick in Data Warehouse: it has a visibility score of 6.5 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 dbt if…
- You want the brand AI engines rank higher (6.5 vs 0.9)
- You need a self-hosted or on-premises option
- You need SOC 2 Type II or ISO 27001:2022 or ISO 27017:2015 or ISO 27018:2025 or ISO 27701:2019 or ISO 42001:2023 or GDPR or CCPA coverage
- Excellent tool for modern data warehousing
AI visibility side by side
Latest weekly run · Nov 3
| Metric | Apache Spark | dbt |
|---|---|---|
| Visibility score | 0.9% | 6.5% |
| Change vs last week | New | New |
| Rank in Data Warehouse | #26 | #14 |
| Share of voice | 0.5% | 2.3% |
| Mentions this run | 3 | 13 |
| Positive in AI answers | — | 0% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Apache Spark 0pp · dbt 0pp over 0 weekly runs.
Engine by engine
dbt leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
Apache Sparkdbt
Head to head in AI answers
1 engine answers name both brands
0Apache Spark ranked higher
1dbt ranked higher
| Prompt | Engine | Apache Spark | dbt |
|---|---|---|---|
| How do companies store and query business data at scale without a dedicated tool? | ChatGPT | #9 | #8 |
Features, pricing & fit
| Dimension | Apache Spark | dbt |
|---|---|---|
| Pricing model | — | Per seat |
| Entry price | — | Free; paid from $100 per seat / mo |
| Free plan | — | Yes |
| Deployment | Runs on single machines or on clusters: standalone, Hadoop YARN, Apache Mesos or Kubernetes. | Cloud platform, with self-hosted dbt Core and hybrid projects run in the customer's own environment. |
| Compliance | — | SOC 2 Type II, ISO 27001:2022, ISO 27017:2015, ISO 27018:2025, ISO 27701:2019, ISO 42001:2023, GDPR, CCPA |
| Support | — | Documentation, online learning and community, 24x5 support, Priority support with enhanced SLA, Dedicated account management |
| Founded | 2010 | — |
| Headquarters | Wilmington, Delaware | — |
| Employees | 5,000+ | — |
| Funding | — | Series D · $414M raised |
| Best for | Data engineering, data science and machine learning work on single machines or clusters. | Data teams that build SQL-based data transformations and want them tested, version-controlled and governed. |
Plans side by side
Apache Spark
Pricing not available.
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
dbt
- Excellent tool for modern data warehousing
- Cleans and models raw data into analytics-ready tables
- Compatible with many other data and BI tools
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Apache Spark vs dbt”, https://slateindex.ai/compare/apache-spark-vs-dbt, data of Nov 3, 2026. Licence: CC BY 4.0.