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.
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.
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 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.
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.
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
|
Feature / Aspect |
Factory Thread |
Trino (formerly PrestoSQL) |
|---|---|---|
|
Primary Use Case |
Manufacturing data virtualization and workflow analytics |
Distributed SQL query engine for data lakes |
|
Industry Focus |
Manufacturing, especially methods like batch manufacturing, plays a crucial role in optimizing production processes across industries. |
Industry-agnostic |
|
Query Type |
Real-time dashboards and workflows |
Distributed SQL querying across sources |
|
Ease of Use |
Very high – drag-and-drop, low-code, AI prompts |
Requires strong SQL expertise |
|
Security |
Built-in role-based access control |
Depends on external tooling |
|
Integration |
ERP, MES, CRM, SCADA, IIoT |
Broad JDBC/ODBC, Hive, Kafka, etc. |
|
Deployment Options |
Cloud, on-prem, hybrid (edge-ready) |
Self-managed or cloud |
|
Governance |
Centralized governance layer |
External governance needed |
|
Best Fit |
Manufacturers needing real-time integration |
Teams needing federated SQL access |
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.
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
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
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
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
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
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
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
Price: Variable (based on subscription and features required)
Features:
Integrates with over 50 data sources
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
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
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
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.
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.
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.
AWS Redshift scales by supporting unlimited concurrent users and queries, so businesses can scale as data grows.
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.
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.