Data Engineering & Analytics Infrastructure
Apache Spark
Distributed analytics engine with machine learning
- SF Score
- 8.3/10
- Pricing
- Free
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2 of 4 tools selected
Data Engineering & Analytics Infrastructure
Starburst
Federated analytics for AI-ready data
- SF Score
- 8.5/10
- Pricing
- From $0.5/mo
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Compare features, strengths, trade-offs, and best-fit use cases.
Comparing
Where each one leads
Read straight from the published figures below — not a ranking, and not a recommendation.
Lowest starting price
Apache SparkFree to start
2 of 2 have a published entry price
Highest SF Score
Starburst8.5/10
2 of 2 are scored
Only one with a free plan
Apache SparkFree plan
free-plan status established for all 2
At a glance
The short version
The facts each tool publishes, lined up so you can scan down a column. Anything the catalog does not establish is marked as such rather than counted against the tool.
Apache Spark
- SF Score
- 8.3 / 10
- Starts at
- $0/mo
- Free plan
- Available
- Category
- AI Analytics Software › Data Engineering & Analytics Infrastructure
Starburst
- SF Score
- 8.5 / 10
- Starts at
- $0.5/mo
- Free plan
- Not offered
- Category
- AI Analytics Software › Data Engineering & Analytics Infrastructure
Key differences
Where these tools actually diverge
The decision dimensions that separate this set, with each tool's own position stated plainly. No winner is declared — fit depends on your workflow.
Positioned for
Apache Spark
Data Engineering & Analytics Infrastructure
Starburst
Data Engineering & Analytics Infrastructure
Pricing model
Apache Spark
Free
Starburst
From $0.5/mo
SoftFinders Score
Apache Spark
8.3 / 10
Starburst
8.5 / 10
Feature comparison
Capability matrix
Grouped by area. Every state carries a word as well as a colour, and anything the catalog has not established stays neutral rather than counting as a missing feature.
4 rows differ 1 identical
| Capability | | |
|---|---|---|
| Overview | ||
| SF Score | 8.3 / 10Value | 8.5 / 10Value |
| Pricing model | FreeValue | PaidValue |
| Starts at | $0/moValue | $0.5/moValue |
| Free plan | Yes | No |
| Category | AI Analytics Software › Data Engineering & Analytics InfrastructureValue | AI Analytics Software › Data Engineering & Analytics InfrastructureValue |
Scroll the matrix sideways to see every column.
Listed by one tool only
12 capabilities are published by a single tool in this set. That does not mean the others lack them — only that the catalog has not established an answer either way.
- Distributed ProcessingRun large jobs across compute clusters
- Spark SQLQuery structured data with SQL syntax
- MLlib LibraryBuild scalable machine learning pipelines securely
- Structured StreamingProcess streaming data with incremental computation
- GraphX AnalyticsAnalyze graph data at distributed scale
- Language SupportUse Python, Scala, Java, or R
- Federated QueriesQuery data across multiple source systems
- Trino EngineUse enterprise features around open Trino
- Access ControlsApply security across distributed data sources
- Query OptimizationImprove performance across federated workloads securely
- AI Data AccessSupport governed data access for AI
- Lakehouse SupportAnalyze open table formats and lakes
Use cases
What each tool is built for
The workflows each tool publishes for itself. Cards appear only for tools that list use cases.
Apache Spark
- Process large datasets across compute clusters
- Build machine learning pipelines with MLlib
- Run structured streaming data transformations
- Query lakehouse tables using Spark SQL
- Support data engineering platform workloads
+3 more on the Apache Spark profile
Starburst
- Query data across distributed environments
- Support AI applications with governed data
- Modernize analytics without full migration
- Accelerate lakehouse analytics for teams
- Unify access across cloud data platforms
+1 more on the Starburst profile
Pros & cons
Strengths and compromises
Each tool's published strengths and the compromises that come with them, as supplied — not scored or ranked against each other. Longer lists fold; nothing is left out.
Apache Spark
Pros
- Scales large data workloads across distributed clusters
- Supports multiple languages for analytical data workloads
- Open-source ecosystem reduces direct vendor lock-in risks
- Multiple cluster managers are officially supported
- Includes SQL ML and graph libraries
Cons
- Requires engineering expertise to operate well in production
- Not a business-user analytics application by default
- Cluster costs need careful ongoing management discipline
- Authentication is not enabled by default
- SparkR is deprecated in version 4.2
- Spark Connect differs from Classic execution semantics
Starburst
Pros
- Federated access reduces unnecessary data movement across teams
- Built on Trino with enterprise governance controls
- Useful for AI-ready distributed data access programs
Cons
- Requires data architecture planning before production rollout
- Not a complete BI dashboarding platform alone
- Credit pricing needs workload monitoring discipline at scale
Pricing
What each one costs
Published pricing signals only. Where a tool has not established a price, that is what it says — never a zero and never "free".
Apache Spark
- Model
- Free
Open-source Apache project; infrastructure costs depend on hosting and managed services.
See full pricingStarburst
- Model
- Paid
Usage-based pricing starts at $0.50 per credit; enterprise options add governance and support.
See full pricingNext step
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