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
What is Starburst?
Starburst is a Trino-based data lakehouse and federated analytics platform that lets organizations query, govern, and analyze data across object storage, databases, warehouses, and other sources without requiring all data to be centralized first. It is available as the fully managed Starburst Galaxy service and the self-managed Starburst Enterprise platform.
- Analytics role: federated SQL queries can access and join data across multiple sources in a single query, while Starburst adds enterprise governance, workload management, and performance capabilities around Trino. Galaxy manages the underlying service in the cloud, whereas Starburst Enterprise can run across cloud, hybrid, on-premises, and Kubernetes environments.
- AI capabilities: Starburst AI supports LLM functions, embeddings, and retrieval-augmented generation workflows. AIDA, now generally available, converts natural-language questions into SQL and analyzes governed data products conversationally. Galaxy also provides an MCP server that lets compatible AI assistants discover permitted data and execute read-only queries, although MCP remains public preview as of August 2026.
- Implementation checks: buyers should assess connector coverage, Trino and SQL expertise, access-control requirements, query performance, network architecture, deployment model, and whether federation is preferable to moving selected datasets into a centralized lakehouse. For AI workloads, teams should also review supported model integrations, privileges, and MCP restrictions.
- Commercial considerations: Starburst Galaxy offers Free, Pro, Enterprise, and Mission-Critical tiers. Pro starts at $0.50 per credit, Enterprise at $0.75, and Mission-Critical at $1.00, although actual credit prices vary by cloud provider and region. AIDA is included in applicable higher-tier configurations, with its token usage billed separately from Galaxy compute.
Starburst is particularly relevant to organizations that want governed analytics and AI access across distributed data while avoiding mandatory consolidation of every source into a single data platform.
Apache Spark
What is Apache Spark?
Apache Spark is an open-source distributed computing engine for running data workloads locally or across clusters. Rather than being a packaged BI or no-code analytics product, Spark provides the processing layer developers use for batch data pipelines, SQL analytics, streaming, data science, and machine learning. Spark SQL and DataFrames provide the main structured-data APIs, while Structured Streaming runs incremental stream-processing workloads on the same Spark SQL engine. MLlib adds scalable algorithms for classification, regression, clustering, recommendation, feature engineering, and machine learning pipelines.
- Best fit: Data engineering, analytics engineering, and ML teams that need programmable computation over datasets too large or demanding for a single-machine workflow. Spark applications can run using Spark’s own Standalone cluster manager, Hadoop YARN, or Kubernetes, while the same framework can also run locally for development and smaller workloads. Spark Connect provides a separate client-server architecture for applications that need remote DataFrame access to a Spark cluster without running the client in the same process as the Spark driver.
- Check first: Apache Spark is licensed under the Apache License 2.0 and does not itself carry a SaaS subscription fee. However, Spark is an execution engine rather than a managed cloud service. Organizations operating their own clusters must provide and manage the surrounding compute, configuration, monitoring, scaling, and security; Spark’s Standalone documentation specifically notes that authentication is not enabled by default. Managed Spark services can take over portions of that operational work, but their infrastructure and service charges are separate from Spark itself.
Bottom line: Apache Spark belongs on the shortlist when the requirement is programmable processing of large batch or streaming datasets across distributed compute—not when users primarily need a ready-made dashboard, self-service BI interface, or no-code AI application.
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.