Data Engineering & Analytics Infrastructure
Starburst
SF 8.5Federated analytics for AI-ready data
From $0.5/moUsage-based pricing starts at $0.50 per credit; enterprise options add governance and support.
Data Engineering & Analytics Infrastructure
Apache Spark
SF 8.3Distributed analytics engine with machine learning
FreeOpen-source Apache project; infrastructure costs depend on hosting and managed services.
Quick decision guide
Choose based on your workflow
Starburst may fit better if...
- Federated Queries
- Trino Engine
- AI Data Access
Apache Spark may fit better if...
- Distributed Processing
- Spark SQL
- MLlib Library
Overview
How each tool is described
Starburst
Starburst is an analytics data infrastructure tool for teams querying data across environments.
It helps teams turn analytics work into clearer decisions while keeping the output easier for non-technical users to understand. The strongest value appears when the team has reliable data, clear ownership, and repeatable questions that need faster answers. Before choosing it, test one real workflow, one messy data source, and one stakeholder review. That shows whether the platform reduces confusion or simply adds another place to manage analytics work. This matters more than a long feature list.
- Best fit: Teams querying data across environments.
- Check first: data readiness, integrations, pricing, governance, and daily adoption.
Bottom line: Starburst is most useful when its strengths match the analytics work your team repeats often.
Apache Spark
Apache Spark is an analytics data infrastructure tool for engineering teams processing large datasets.
It helps teams turn analytics work into clearer decisions while keeping the output easier for non-technical users to understand. The strongest value appears when the team has reliable data, clear ownership, and repeatable questions that need faster answers. Before choosing it, test one real workflow, one messy data source, and one stakeholder review. That shows whether the platform reduces confusion or simply adds another place to manage analytics work. This matters more than a long feature list.
- Best fit: Engineering teams processing large datasets.
- Check first: data readiness, integrations, pricing, governance, and daily adoption.
Bottom line: Apache Spark is most useful when its strengths match the analytics work your team repeats often.
Side-by-side
Key differences
Criteria
Data Engineering & Analytics InfrastructureStarburst
Data Engineering & Analytics InfrastructureApache Spark
Best for
Data Engineering & Analytics Infrastructure
Data Engineering & Analytics Infrastructure
Score
8.5/10
8.3/10
Pricing
From $0.5/mo
Free
Category / audience
AI Analytics Software › Data Engineering & Analytics Infrastructure
- federated analytics
- Trino platform
- lakehouse analytics
AI Analytics Software › Data Engineering & Analytics Infrastructure
- data engineering
- open-source analytics
- distributed processing
Feature check
Side-by-side feature check
Feature
Starburst
Apache Spark
Federated QueriesQuery data across multiple source systems
-
Trino EngineUse enterprise features around open Trino
-
AI Data AccessSupport governed data access for AI
-
Lakehouse SupportAnalyze open table formats and lakes
-
Access ControlsApply security across distributed data sources
-
Query OptimizationImprove performance across federated workloads securely
-
12 capabilities compared.12 differentiating rows are shown first.
Use cases
Who they're built for
Starburst
- Query data across distributed environmentsAccess lakes, warehouses, and databases without moving everything
- Support AI applications with governed dataProvide trusted cross-source context for models and agents
- Modernize analytics without full migrationReduce data movement while preserving existing source systems
Apache Spark
- Process large datasets across compute clustersRun batch analytics workloads beyond single-machine capacity for teams
- Build machine learning pipelines with MLlibTrain scalable models using Spark machine learning tools
- Run structured streaming data transformationsHandle continuous data processing with incremental computation for teams
The trade-offs
Pros & cons of each tool
Trade-offs
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
Trade-offs
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
Cons
- Requires engineering expertise to operate well in production
- Not a business-user analytics application by default
- Cluster costs need careful ongoing management discipline
Final verdict
Best fit depends on your workflow
Current catalog data shows meaningful overlap between Starburst and Apache Spark. Use the signals below to decide based on workflow, ecosystem, pricing, and implementation fit.
Differentiators available
Starburst has 4 visible decision signals and Apache Spark has 4.
Score signal
Starburst has the higher SoftFinders Score in the current catalog data.
Choose Starburst if…
- Query data across distributed environments
- Support AI applications with governed data
- Modernize analytics without full migration
- Federated Queries
Choose Apache Spark if…
- Process large datasets across compute clusters
- Build machine learning pipelines with MLlib
- Run structured streaming data transformations
- Distributed Processing
TRY THEM YOURSELF
See which one fits your workflow
Both tools have their strengths, the best way to decide is to spend a few minutes inside each.