Top Trino Alternatives for Enhanced Query Performance and Flexibility

Looking for Trino alternatives for data querying and processing?

Here are the top 10 platforms that excel in performance, scalability, cost and integration. Find out which tools can meet your data management needs, including various Trino alternatives.

Overview of Trino Platform

Trino is an open-source, distributed SQL query engine optimized for running fast, interactive analytics on large datasets. It allows users to query data across multiple heterogeneous data sources, including data lakes, relational databases, and NoSQL systems, using a single SQL interface. Trino's architecture supports high performance and scalability, making it suitable for complex analytical workloads in modern data environments.

10 Best Trino Alternatives & Competitors (for 2025)

When choosing a Trino alternative, you need to consider performance, scalability, cost and integration to match your data management requirements. Here are the top 10 alternatives and what they’re good at:

• Best for Manufacturing Data Virtualization: Factory Thread

• Best for Serverless Data Warehousing: Google Cloud BigQuery

• Best for Scalable Cloud Data Warehousing: AWS Redshift

• Best for In-Memory Data Processing: Apache Spark

• Best for Stream Processing: Apache Flink

• Best for Managed Big Data Frameworks: Amazon EMR

• Best for Enterprise-Grade Analytics: Microsoft Azure HDInsight

• Best for Cost-Efficient Cluster Management: Google Cloud Dataproc

• Best for Unified Data Access: Starburst

• Best for Multi-Model Data Management: InterSystems IRIS

Factory Thread - Best for Manufacturing Data Virtualization

Factory Thread is a modern data virtualization platform tailored specifically for manufacturing environments. It serves as a logical data layer that unifies production, quality, and business system data—eliminating the need for data duplication and enabling fast, secure access.

Why Factory Thread is a Top Trino Alternative in 2025

While Trino offers broad data federation across sources, Factory Thread is optimized for real-time manufacturing data workflows. It includes prebuilt connectors for ERP, MES, CRM, and IoT systems and supports secure role-based access. With a low-code visual designer and AI-powered integration capabilities, it’s ideal for teams seeking rapid deployment of operational analytics—without needing advanced query language expertise. For more information on how Factory Thread compares to other alternatives to Denodo for data virtualization, you can read a detailed comparison.

What Factory Thread is Ideal For

Factory Thread is best suited for:

• Real-time virtualization of production and quality data

• Connecting ERP, MES, and CRM systems with minimal data duplication

• Providing a secure, governed data layer for manufacturing teams

• Empowering non-technical users with drag-and-drop data tools

• Deploying integrated analytics without large data engineering teams

Factory Thread vs Trino: 2025 Comparison

Summary:

• Factory Thread is ideal for manufacturers who need real-time visibility, minimal overhead, and domain-specific data integration.

• Trino is better for organizations focused on federated SQL access across a range of distributed data sources with internal engineering capacity.

Google Cloud BigQuery - Best for Serverless Data Warehousing

Price: Pricing is based on the amount of data processed and stored. On-demand pricing is $5 per TB of data processed, while flat-rate pricing starts at $2,000 per month for dedicated resources.

Features:

• Fully managed, serverless data warehouse

• Supports a variant of SQL for querying in a data lakehouse

Pros:

• Fast SQL queries on large datasets

• Automatic scaling

• Built-in machine learning capabilities

• Robust security features

Cons:

• Complexity for newcomers

• Limited flexibility compared to on-premises solutions

Google Cloud BigQuery, a fully managed serverless data warehouse, can handle large datasets quickly and process massive amounts of structured and unstructured data at speeds of up to trillions of rows per second, making it perfect for rapid data analysis. Integration with other Google Cloud services makes data workflows seamless and data architecture better.

BigQuery’s powerful querying and built-in machine learning features are great, but complexity can be tough for newcomers. As a fully managed service, it lacks the flexibility of traditional on-premises solutions. But automatic scaling and robust security features make it a top choice for analyzing large datasets.

Rating:

• Price: 4/5

• Design: 4.5/5

• Usability: 4/5

• Features: 5/5

AWS Redshift - Best for Scalable Cloud Data Warehousing

Price: Variable (based on usage and instance types)

High-performance query processing with columnar storage and data compression supports virtually unlimited concurrent users and queries, and enhances query performance.

Pros:

• High scalability for concurrent users

• Enhanced performance with materialized views

• Integration with other AWS services

Cons:

• Complexity in setup and management

• Higher costs for large-scale operations

AWS Redshift, a cloud-native data warehouse, is designed for scalability and performance. High-performance query processing techniques like columnar storage and data compression reduces I/O operations, making it perfect for big data warehouses.

Supports virtually unlimited concurrent users and queries, Redshift scales with growing data needs through massively parallel processing. Materialized views improves performance for repetitive analytics tasks by providing precomputed query data results, including ray id.Though complex in setup and management with potentially high costs for large-scale operations, AWS Redshift’s integration capabilities and scalability make it a robust choice for cloud data warehousing.

Rating:

• Price: 3.5/5

• Design: 4/5

• Usability: 3.5/5

• Features: 4.5/5

Apache Spark - Best for In-Memory Data Processing

Price: Free

Features:

• Supports multiple languages: Scala, Java, Python, R

• Resilient Distributed Datasets (RDDs) for fault tolerance and parallel processing

Pros:

• Fast

• Multi-language support with PySpark and other APIs

• Flexible and compatible

• Advanced analytics

Cons:

• High memory consumption and hardware costs

• Limited real-time processing

• Steep learning curve

Apache Spark:

• In-memory distributed computing for big data analytics, fast processing of large datasets.

• Multiple languages: Scala, Java, Python, R, very versatile.

• Architecture using Resilient Distributed Datasets (RDDs) for fault tolerance and parallel processing.

Though Spark is fast, high memory consumption and hardware costs are drawbacks. Limited real-time processing and steep learning curve may be challenging. But multi-language support, flexibility and advanced analytics make it a powerful tool for in-memory data processing, better performance.

Rating:

• Price: 5/5

• Design: 4.5/5

• Usability: 3.5/5

• Features: 4.5/5

Apache Flink - Best for Stream Processing

Price: Free

Features:

• Distributed architecture for high availability

• Advanced state management: incremental checkpointing

Pros:

• Real-time data stream processing

• High fault tolerance with state management

• Event-time processing for out-of-order data analysis

Cons:

• Complex setup and development

• Less community support compared to popular frameworks

Apache Flink:

• Real-time data stream processing for applications that need immediate insights. Distributed architecture for high availability, smooth operation during peak demand. Advanced state management: incremental checkpointing for fault tolerance and performance.Flink’s event-time process accurately analyzes out-of-order data. But complex setup and development, and less community support can be tough. Despite these, Flink’s real-time data stream processing is essential for stream processing applications.

Rating:

• Price: 5/5

• Design: 4/5

• Usability: 3.5/5

• Features: 4.5/5

Amazon EMR - Best for Managed Big Data Frameworks

Price: Variable (based on usage and instance types) Features:

• Integration with AWS services

• Cost-effective big data management

Pros:

• Managed big data frameworks: Hadoop and Spark

• Scalable based on user needs

• Integration with other AWS services

Cons:

• Complex setup

• High costs for large usage

Amazon EMR has:

• Manages big data frameworks: Hadoop and Spark

• Integrates with AWS services for more functionality and performance

• Cost-effective management to scale as needed

• For large datasets

• Scales up and down to manage resources as demand grows

Though Amazon EMR is good for big data workloads, complex setup and high costs for extensive use can be challenging. But integration with other AWS services and scalability make it a good choice for managed big data frameworks.

Rating:

• Price: 3.5/5

• Design: 4/5

• Usability: 3.5/5

• Features: 4/5

Microsoft Azure HDInsight - Best for Enterprise-Grade Analytics

Price: Variable (based on usage and instance types)

Features:

• Open-source frameworks: Apache Hadoop, Apache Hudi, Spark

• Over 30 industry certifications for security and compliance

Pros:

• Enterprise-grade analytics with security

• Integration with other Azure services

• Autoscaling for cost management

Cons:

• Complex setup and management

• High costs for large operations

Microsoft Azure HDInsight allows users to run open-source frameworks like Apache Hadoop, Apache Hudi, and Spark without managing infrastructure. For enterprise-grade use, it has enhanced security and over 30 industry certifications, perfect for big organizations.Integration with Azure services like Azure Synapse Analytics and Azure Data Lake Storage adds more capabilities, including delta lake and data lakes. Autoscaling helps manage costs by allowing users to pay only for what they use.

Though complex to set up and manage with high costs for large operations, Microsoft Azure HDInsight’s enterprise-grade analytics and security is perfect for big organizations.

Rating:

• Price: 3.5/5

• Design: 4/5

• Usability: 3.5/5

• Features: 4.5/5

Google Cloud Dataproc - Best for Cost-Efficient Cluster Management

Price: 1 cent per virtual CPU per hour

Features:

• Cluster creation, scaling and shutdown in under 90 seconds

• Integration with Google Cloud services

Pros:

• Cost Efficiency

• Fast Deployment

• Full Platform

• Easy Management

Cons:

• Limited to Google Cloud ecosystem

• Complex setup for newbies

Google Cloud Dataproc, a fully managed service, has:

• Runs Apache Spark and Hadoop clusters

• 1 cent per virtual CPU per hour pricing for big data workloads

• Create, scale and shut down clusters in under 90 seconds, much faster than traditional setup

Dataproc’s ease of management and fast deployment are big advantages, but limited to Google Cloud ecosystem can be a drawback. Setup complexity for newbies may be challenging. Despite these, Google Cloud Dataproc’s cost-efficient cluster management is a valuable tool for big data.

Rating:

• Price: 5/5

• Design: 4.5/5

• Usability: 4/5

• Features: 4.5/5

Starburst - Best for Unified Data Access

Price: Variable (based on subscription and features required)

Features:

• Integrates with over 50 data sources

• Federated access to distributed and hybrid data

Pros:

• Fast and scalable data access without data duplication

• Single point of governance for security policies

• Same technical features as Trino

Cons:

• Federated access setup complexity

• High costs for many integrations

Starburst has:* Federated access to distributed and hybrid data without migrations, perfect for unified data access.

• Fast, governed and scalable data access across multiple environments, no data duplication and real-time analytics.

• Integrates with over 50 data sources, flexibility and data workflows.

Starburst’s single point of governance enforces security policies across all connected data sources. However, federated access setup and high costs for many integrations can be tricky. Despite these, Starburst’s unified data access is a powerful tool for data management.

Rating:

• Price: 3.5/5

• Design: 4/5

• Usability: 4/5

• Features: 4.5/5

InterSystems IRIS - Best for Multi-Model Data Management

Price: Variable (based on subscription and features required)

Features:

• Multi-model data management and analytics

• Application development, interoperability and open analytics

Pros:

• Better data management performance

• Multiple data sources and types

• Integration for data workflows

Cons:

• Complex setup and management

• High costs for heavy use

InterSystems IRIS performance and deployment of analytics applications through multi-model data management and relational databases analytics. With application development, interoperability and open analytics capabilities, it’s a full solution for multiple databases data sources.

InterSystems IRIS integrates with multiple data sources, data workflows and efficient management. However, complex setup and high costs for heavy use can be challenging. Despite these, InterSystems IRIS’s multi-model data management is a valuable tool for managing and analyzing multiple data sources.

Rating:

• Price: 3.5/5

• Design: 4/5

• Usability: 3.5/5

• Features: 4.5/5

How to Choose the Best Trino Alternative

To choose the best Trino alternative consider:

• Performance and scalability is key.

• High-performance query engines that can handle many users and fast analysis are essential.

• For fast data processing, platforms like Apache Spark or Google Cloud BigQuery might be good.And integration with existing tools and platforms to integrate analytics workflows. Starburst and InterSystems IRIS have robust integration, data workflows and management.

Also, consider the cost and flexibility. Fully managed services like BigQuery is easy to use but may have limited flexibility compared to on-premises solutions.

And data governance practices, access control and data lineage tracking when adopting a new data analytics solution. Starburst has centralized data governance, security policies across all connected data sources.

By evaluating these factors you can choose the best Trino alternative for your organization.

Conclusion

Choosing a data analytics platform can be overwhelming but understanding the strengths and limitations of each option can make the process more manageable. From serverless data warehousing with Google Cloud BigQuery to scalable cloud data warehousing with AWS Redshift, in-memory data processing with Apache Spark, each has its own advantages for different organizations.

Ultimately the best Trino alternative for your organization will depend on your requirements, real-time data stream processing with Apache Flink, cost-efficient cluster management with Google Cloud Dataproc or multi-model data management with InterSystems IRIS. By considering performance, scalability, cost, integration capabilities and data governance practices you can make an informed decision to boost your data management and analytics.

FAQs

What is the main benefit of Google Cloud BigQuery?

The main benefit of Google Cloud BigQuery is fast SQL queries on large datasets due to its fully managed, serverless architecture which is essential for businesses that need fast data analysis.

How does AWS Redshift scale?

AWS Redshift scales by supporting unlimited concurrent users and queries, so businesses can scale as data grows.

Why is Apache Spark good for in-memory data processing?

Apache Spark is good for in-memory data processing because it uses in-memory distributed computing which allows fast processing of large datasets and flexibility with support for multiple programming languages.

Why is Starburst the best for unified data access?

Starburst is the best for unified data access because it provides federated access to distributed and hybrid data without migrations, fast, governed and scalable access.

What to consider when choosing a Trino alternative?Consider