Apache Spark vs Trino

Apache Spark8.9Visit Trino8.5Visit

Higher score Apache Spark #4 in Big Data Analytics Software 8.9/10 Free plan Free plan✓ 0 of 1 features Visit Apache Spark

vs

Trino #8 in Big Data Analytics Software 8.5/10 Free plan Free plan✓ 0 of 1 features Visit Trino

• Apache Spark fits best

  Teams building flexible distributed data pipelines

• Trino fits best

  Teams needing open-source federated SQL

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Side by side

Feature	Apache Spark 8.9/10 Visit ↗	Trino 8.5/10 Visit ↗
At a glance
Editor score	8.9	8.5
Ranking	#4 in Big Data Analytics Software	#8 in Big Data Analytics Software
Best for	Teams building flexible distributed data pipelines	Teams needing open-source federated SQL
Pricing model	Free	Free
Starting price	Free plan	Free plan
Free plan	✓	✓
Free trial	—	—
Deployment	Self-hosted	Cloud, Self-hosted
Platforms	Windows, macOS, Linux	Linux
Support	Community, Docs	Community, Docs
Integrations	20 integrations	6 integrations
Built for	Small business, Mid-market, Enterprise	Mid-market, Enterprise
Features Apache Spark 0/1 · Trino 0/1
Built-in visualization	Not checked yet	Not checked yet
Specs
Deployment	Not checked yet	Not checked yet
Processing model	Not checked yet	Not checked yet
Documented data scale	Not checked yet	Not checked yet
Data source connectors	Not checked yet	Not checked yet
Query languages	Not checked yet	Not checked yet
Our review
Pros	Processes batch and streaming workloads with SQL and DataFrame APIs Supports Python, SQL, Scala, Java, and R interfaces Runs on Standalone, YARN, or Kubernetes and connects to varied data sources	Queries and joins data across multiple systems without centralizing it first Distributed parallel execution supports interactive analytics and batch ETL Offers REST access, query monitoring, and Ranger-based access controls
Cons	Self-hosted deployment requires teams to manage their environment Not a managed warehouse or hosted ETL service Support is through community resources and documentation	Requires deployment and cluster operation in cloud or self-hosted environments Connector coverage varies by system, and the listed integrations are limited Support channels are community and documentation
Our verdict	Apache Spark is an open-source distributed computing engine for large-scale data processing and analytics. Developers, data engineers, analysts, and organizations processing data across clusters can use it for batch workloads, incremental… Read the review →	Trino is an open-source distributed SQL query engine for teams that need to analyze data across multiple systems without first copying it into one store. Its connectors span relational and NoSQL databases, object storage, streaming… Read the review →

Strengths and trade-offs

• Apache Spark — where it wins

  • Processes batch and streaming workloads with SQL and DataFrame APIs
  • Supports Python, SQL, Scala, Java, and R interfaces
  • Runs on Standalone, YARN, or Kubernetes and connects to varied data sources

  Where it doesn't

  • Self-hosted deployment requires teams to manage their environment
  • Not a managed warehouse or hosted ETL service
  • Support is through community resources and documentation

• Trino — where it wins

  • Queries and joins data across multiple systems without centralizing it first
  • Distributed parallel execution supports interactive analytics and batch ETL
  • Offers REST access, query monitoring, and Ranger-based access controls

  Where it doesn't

  • Requires deployment and cluster operation in cloud or self-hosted environments
  • Connector coverage varies by system, and the listed integrations are limited
  • Support channels are community and documentation

• Apache Spark8.9/10 · Free plan

  Flexible distributed processing for teams building batch and streaming data pipelines.

  Visit Apache SparkFull verdict →
• Trino8.5/10 · Free plan

  Query and join data across connected systems with distributed ANSI SQL.

  Visit TrinoFull verdict →