Starburst, a major provider of enterprise platform offerings designed to optimize the Trino distributed SQL query engine, recently celebrated a milestone tied to the open-source code family that underpins the engine’s development. Trino, an open-source, highly parallel distributed SQL query engine, is built to deliver interactive analytics across vast data volumes. In a recent interview, venture-backed insights publisher VentureBeat spoke with Dain Sundstrom, co-creator of the project, to reflect on the journey, the current state, and the path ahead for Trino.
Open Source lineage and milestone
Ten years have passed since the inception of the original Presto/Trino open-source code family, a collaboration launched by Dain Sundstrom alongside Martin Traverso, David Phillips, and Eric Hwang at Facebook. The goal was clear and ambitious: to solve the persistent challenge of analytics and querying at speed across Facebook’s enormous data assets. The ambition was not merely to create a fast engine but to foster a scalable community-driven approach to analytics on large-scale datasets. The early days were defined by a shared vision among the founders to empower internal data scientists and engineers to extract insights rapidly from sprawling data stores.
In 2018, a pivotal split occurred. The original code family diverged into two distinct lineages: PrestoDB, which remained affiliated with Facebook, and PrestoSQL, the version steered by the original creators. This bifurcation signaled a broader shift in governance and development focus. The team behind PrestoSQL charted a path that emphasized openness and community-led progression beyond the confines of a single corporate entity. The latter lineage, under steady development and community contributions, ultimately adopted a new identity. In December 2020, the PrestoSQL codebase was rebranded to Trino, marking a formal and public capitalization of a brand that would come to symbolize a broad ecosystem of contributors, connectors, and enterprise users beyond its original Facebook roots.
This milestone is more than branding; it marks a moment of reaffirmation for open-source values. Sundstrom emphasized that the decision to open-source the project arose from a shared history of collaboration among the core creators. The ethos of openness not only accelerated development but also invited a wider set of contributors to participate in the problem-solving process. The transition from a closed or semi-closed corporate project to a fully open, community-driven project carried with it benefits and challenges alike. The open-source lineage became a living organism, continually shaped by the input of developers around the world and by real-world usage across a spectrum of industries.
The narrative of Trino’s origins also highlights the tension between rapid internal innovation and the discipline required to sustain a healthy external ecosystem. The Presto/Trino family demonstrated that speed in analytics could coexist with community-driven governance when the right structures, processes, and cultural commitments are in place. The 2018 split and the later rebranding to Trino did not erase the core idea: an engine capable of performing sophisticated queries across diverse data sources with low latency. Instead, it amplified the opportunity for broader adoption and collaboration, inviting a more vibrant ecosystem of connectors, integrations, and use-case-driven enhancements.
Looking back, the lineage embodies a microcosm of the broader open-source movement: a project born inside a large-scale consumer platform evolves into a platform-agnostic, community-focused technology that serves enterprises across sectors. The milestone anniversary is thus not simply a historical footnote but a signal of maturation, resilience, and the ongoing relevance of the original problem the project sought to solve: delivering fast, scalable analytics to users who require timely insights from massive datasets.
Continued refinements and architectural evolution
From its inception, Trino’s core objective was to enable fast, interactive querying across large-scale data. Early iterations prioritized performance, but as the ecosystem matured, it became essential to embed a comprehensive set of capabilities that enterprise users now consider foundational. Security grew from a desirable feature to a central pillar of the project. Where early releases paid scant attention to in-bounds of data access, current versions of Trino integrate robust security controls as a default expectation. Enterprises rely on strong authentication, authorization, governance, and audit capabilities to meet regulatory requirements and internal policies. This emphasis on security is not incidental; it reflects a recognition that analytics spanning multiple data silos must be both fast and trustworthy.
The ecosystem around Trino has expanded tremendously. The number of supported data connectors has grown in tandem with the engine’s demand for interoperability. Beyond traditional relational data sources such as PostgreSQL, Oracle, and SQL Server, Trino now supports connectors to non-relational and semi-structured sources. Notably, it includes Elasticsearch, OpenSearch, MongoDB, and Apache Kafka, enabling streaming and real-time data manipulation alongside batch-style analytics. The expansion of connectors has broadened the potential workloads and data domains that Trino can tackle, reinforcing its role as a unifying query layer across heterogeneous data landscapes.
In parallel with connector growth, there has been continued refinement of the function language and execution model. Sundstrom and the team have prioritized extensibility—making it simpler for developers to add new functionality through user-defined functions and other extension points. This design choice enables more complex analytical workflows to be implemented directly within Trino, reducing the need for stitching together disparate tools. The improvements to ETL (extract, transform, load) workflows address one of the most common enterprise pain points: making data movement and transformation more efficient and more accessible to non-experts. By focusing on “out-of-the-box” productivity, the project aims to empower a broader spectrum of users—data engineers, analysts, and business stakeholders alike—to derive value without requiring specialized expertise in every case.
The decision to embrace open-source development was shaped by shared experiences among the core creators and contributors. The team recognized early on that the transformation of data analytics demanded not only a fast engine but also an ecosystem that could adapt to evolving requirements. With a growing community, the project faced two principal challenges: scaling the system in terms of engineering complexity and scaling the community to sustain collaboration. The first challenge was the mechanical problem of improving performance, reliability, and compatibility across diverse infrastructures. The second challenge involved fostering effective communication and coordination among a distributed group of contributors who work asynchronously across time zones, languages, and corporate boundaries. The Trino journey has thus become as much about enabling collaboration as it is about delivering technical features.
In this evolution, security, performance, governance, and ecosystem breadth have formed a virtuous cycle. As connectors expanded, the engine could address more use cases, which in turn attracted more contributors. More contributors spurred additional innovations, including more secure data access patterns, improved optimization strategies, and enhanced user experiences. The result is a feedback loop that sustains a dynamic, thriving ecosystem capable of addressing both current needs and future challenges.
Design choices that support enterprise-scale analytics
A central design consideration in Trino’s ongoing refinements is its capacity to scale not only in software but also in community and governance. The distributed nature of the engine, where queries are executed across multiple worker nodes, is complemented by a scalable planning and optimization layer that can coordinate work efficiently even as data volumes grow. The architecture emphasizes parallelism, fault tolerance, and deterministic behavior under concurrent workloads. The security model is layered to support enterprise-grade controls without sacrificing performance, a non-trivial balance given the complexity of multi-tenant environments.
Additionally, the project emphasizes compatibility with existing data ecosystems. By supporting familiar SQL semantics and established connectors, Trino reduces integration friction for teams migrating from traditional data warehouses or other query engines. This compatibility is paired with performance optimizations and execution strategies that minimize latency, enabling interactive analytics at scale. In practice, these architectural choices translate into faster time-to-insight for analysts and data scientists who need to iterate quickly on hypotheses and dashboards.
The open-source lineage and the ongoing refinements together explain why the Trino ecosystem has sustained interest from both individual contributors and large organizations. The combination of a powerful, extensible engine and a broad, growing set of integrations helps explain why Trino remains relevant across a wide range of industries and data architectures. The project’s ability to adapt to new data types, connectors, and workloads—while maintaining reliability and performance—reflects a mature approach to continuous improvement that is valued by enterprise teams seeking dependable analytics on diversified data assets.
Ecosystem growth: connectors, tooling, and interoperability
A defining feature of Trino is its expansive connector ecosystem. Connectors enable Trino to reach across data silos, turning disparate data stores into a unified query surface. This capability is especially valuable to enterprises that operate heterogeneous data environments, where data resides in multiple databases, data lakes, or streaming platforms. The connectors to relational databases such as PostgreSQL, Oracle, and SQL Server allow analysts to join transactions and operational data with analytical workloads in a single query engine. This cross-source querying capability eliminates the need for expensive extract-transform-load (ETL) pipelines that duplicate data into a single repository for analysis.
In addition to traditional relational sources, Trino supports a range of non-relational data stores that are increasingly central to modern data architectures. Elasticsearch and OpenSearch enable efficient search and analytics over semi-structured and text-rich data. MongoDB offers document-based storage with flexible schemas, while Apache Kafka is a critical streaming platform for real-time data ingestion and event-driven analytics. By including these connectors, Trino can handle a continuum of data—from batch-oriented warehouse-style datasets to real-time streams—within a single, unified SQL interface. This interoperability is a major driver of productivity for data teams, as it enables more comprehensive analyses without the overhead of re-architecting data pipelines around a single technology.
Beyond connectors, the Trino ecosystem has grown with tooling that supports governance, monitoring, and optimization. Enterprises seeking to operationalize analytics appreciate solutions that provide visibility into query performance, resource utilization, and data lineage. While the core project emphasizes efficient query execution, the surrounding tooling helps teams manage cost, track compliance, and enforce data access policies. The expansion of tooling is not merely about convenience; it is essential for deploying Trino at scale in production environments where reliability and oversight matter.
In practice, organizations that adopt Trino often report improvements in data exploration speed, reduced data duplication, and greater flexibility in how data is accessed for analysis. For teams working with both legacy data warehouses and modern data lakes, Trino’s multi-source capabilities reduce the time and effort required to extract insights. This is especially valuable for data-intensive businesses that rely on timely decision-making and the ability to test new hypotheses quickly. The ecosystem’s breadth also lowers the barrier to bringing new data stores into the analytics framework, as teams can add connectors as needed without committing to a complete overhaul of their data infrastructure.
Real-time analytics and geo-spatial capabilities
One area where Trino has garnered particular attention is its performance with real-time data and geospatial analytics. Real-time use cases—such as those handled by dispatch services, ride-sharing platforms, and on-demand delivery networks—demand ultra-low-latency queries over large, continuously updating datasets. Trino’s architecture is well-suited to these workloads because it can push computation closer to where data resides and aggregate results rapidly across distributed nodes. In this context, Trino’s ability to query across multiple data sources without moving data into a single repository helps organizations maintain fresh insights while avoiding costly data duplication.
Geospatial analytics, in particular, have emerged as a notable strength. Trino is recognized for handling geospatial data effectively, a capability that is increasingly important as location-aware services become more prevalent. Applications spanning mapping, logistics, cellular network analysis, and location-based marketing benefit from the engine’s capacity to process and analyze geospatial data at scale. The combination of real-time capabilities and robust geospatial support positions Trino as a versatile tool for organizations whose data has a strong spatial dimension.
Real-world use cases and enterprise adoption
Trino is deployed by a diverse set of companies for internal analytics, exemplifying its versatility and performance in production environments. Netflix and LinkedIn are two prominent users that rely on Trino for internal analytics, enabling fast queries across substantial data stores. It is common for large organizations to contribute to the open-source project, recognizing that shared improvements benefit all participants. For instance, Bloomberg and Comcast have contributed to Trino’s ecosystem, reflecting a collaborative approach where large enterprises invest in community-driven development to advance capabilities that matter to their own data workloads.
Sundstrom described how Trino’s design resonates particularly well with real-time and near-real-time workloads typical of services with high-velocity data. In such environments, the engine’s low-latency query execution supports rapid decision-making, operational visibility, and timely reporting. Real-time analytics are especially valuable for customer-facing platforms that require near-instant responses or quick adjustments to services based on current data. Moreover, the platform’s extensibility and broad connector portfolio make it attractive for organizations seeking to unify data access across a heterogeneous technology stack. By enabling unified SQL access to disparate data sources, Trino reduces complexity and accelerates analytics workflows.
Trino’s appeal also extends to sectors that handle substantial geospatial data, including ride-sharing networks, logistics providers, and mapping services. The capability to perform fast, location-based queries over large geodata sets enhances the potential for real-time routing, demand forecasting, and coverage analytics. The combination of speed, compatibility, and breadth of data sources helps explain why Trino has gained traction across diverse industries and use cases.
Future trajectory: expansion of workloads and data types
Looking ahead, Sundstrom expresses optimism about Trino’s trajectory as the pace of innovation accelerates alongside expanding use cases and data types. The project’s future roadmap includes broadening the spectrum of workloads it can efficiently tackle. In particular, the addition of geospatial processing capabilities is anticipated to unlock new value for mapping companies, cellular providers, and food-delivery networks that want to analyze customer data through precise spatial relationships. The ability to process and analyze diverse types of data—beyond traditional tabular data—will enable new analytics patterns and insights that organizations can monetize or operationalize in real time.
The community’s demonstrated capability to generate innovative solutions to user problems further reinforces confidence in Trino’s long-term relevance. While it’s challenging to forecast every possible use case, the trajectory suggests continued growth in adoption and diversification of applications. As organizations adopt Trino to support more complex workloads, the engine will likely incorporate enhancements in performance, security, and user experience. The collaborative nature of Trino’s development means that user-driven needs have tangible pathways to influence the product’s evolution, reinforcing the value of an open, participatory model for enterprise analytics.
The sentiment surrounding Trino’s 10-year milestone is one of forward-looking confidence. The platform has evolved from a high-performance internal tool at a single company to a widely adopted, open-source project with a robust ecosystem and a growing enterprise footprint. The community’s ability to respond to changing analytics demands—across industries and data technologies—suggests that Trino will continue to adapt, scale, and meet the evolving needs of data teams worldwide.
The role of the community and ongoing governance
A critical dimension of Trino’s ongoing development is the health and vitality of its community. Sundstrom highlighted the challenge of balancing rapid innovation with open, collaborative governance. The project’s success depends on an active, diverse group of contributors who can address a broad spectrum of data challenges. Building effective communication channels among participants—ranging from independent developers to large enterprise teams—has been essential to ensuring that the community remains aligned around common goals while still embracing diverse perspectives.
The governance model behind Trino emphasizes openness, collaboration, and shared responsibility for the project’s direction. The ability to coordinate across time zones, organizational boundaries, and cultural differences is a core strength of the open-source approach, but it also requires deliberate practices to prevent fragmentation or duplicated effort. The community’s leadership structure, roadmaps, and contribution processes must be designed to accommodate growth while preserving the project’s technical integrity and user-focused priorities. The ongoing effort to manage these dynamics is a hallmark of Trino’s maturity as a distributed, collaborative project.
As adoption expands, the ecosystem faces the age-old tension of innovating quickly while delivering stability for production workloads. This tension is especially acute for enterprise deployments that demand predictable performance, strong security, and reliable interoperability. The community’s experience in navigating this tension will shape how Trino evolves in the coming years, particularly as more organizations rely on it for mission-critical analytics and decision-making.
AI scaling context and enterprise analytics
In the broader landscape of enterprise AI, scaling challenges—such as power caps, rising token costs, and inference delays—are reshaping how organizations approach AI deployment. While these trends are not unique to Trino, they influence how data platforms like Trino integrate with AI workflows. Enterprises increasingly rely on a combination of data querying and model-powered analytics to derive actionable insights, so the ability to access and analyze data quickly across diverse sources becomes a strategic advantage. In this context, Trino’s role as a fast, multi-source SQL engine positions it as a critical enabler for AI-ready data exploration.
The interplay between AI workloads and data analytics underscores the need for robust data access layers that can serve both traditional BI and modern AI pipelines. Trino’s connectors and distributed execution environment support this dual requirement, allowing data scientists and engineers to query across data lakes, data warehouses, and streaming platforms without moving data into a single repository. This capability helps reduce data duplication and accelerates experimentation with AI models, training data preparation, and feature engineering across heterogeneous data stores. As AI systems continue to evolve, the ability to provide timely, governance-aware access to data remains central to scaling responsible and efficient AI initiatives.
The broader AI context thus informs Trino’s continued emphasis on security, governance, and ecosystem breadth. By maintaining robust security controls and broad interoperability, Trino offers a stable foundation for AI-enabled analytics that can adapt to evolving data regimes and usage patterns. The project’s open-source ethos and collaborative governance further support innovation in ways that align with the needs of a wide range of organizations—from startups to large enterprises—who require scalable data access for AI-driven decision-making.
Industry impact and competitive landscape
Within the broader data analytics landscape, Trino’s impact is measured not only by its technical capabilities but also by its ability to harmonize data access across diverse environments. The engine’s open-source lineage, its rapid evolution, and the extensible connectors collectively contribute to a compelling value proposition for enterprises seeking to unify analytics. The collaboration among large and small contributors alike fosters ongoing improvements, ensuring that the platform remains relevant as data architectures shift toward hybrid and multi-cloud deployments.
As part of a larger ecosystem of analytics engines, Trino operates in a competitive landscape that includes alternatives with different design goals and trade-offs. While alternatives may emphasize specialized workloads or simplified deployment models, Trino’s strength lies in its breadth: the capacity to run SQL queries efficiently across a wide array of data sources in real time. This breadth makes it a favorable option for organizations that require a unified query layer across multiple data stores, rather than stitching together disparate systems with bespoke integrations.
The enterprise adoption of Trino also signals a broader trend toward community-driven, standards-based analytics solutions. Instead of relying solely on a single vendor’s suite of tools, organizations are increasingly choosing platforms that can flex with evolving data requirements while maintaining governance and performance. The ongoing success of Trino in this environment is a testament to the power of open-source collaboration, clear design principles, and a commitment to delivering tangible value to users across industries.
The future of Trino and the path forward
Looking ahead, Trino’s trajectory appears poised for continued expansion in both scope and impact. The platform’s core strengths—speed, scalability, and interoperability—will likely be complemented by deeper enhancements in security, governance, and usability. As additional data sources are integrated and more complex workloads are supported, Trino is expected to become even more central to enterprise data architectures that require agile access to information across distributed platforms.
The geospatial processing capability is anticipated to unlock new analytical patterns for organizations across sectors such as mapping, telecommunications, and logistics. By enabling more sophisticated spatial queries and analytics, Trino can empower teams to extract location-based insights that inform operations, strategy, and customer experiences. The continued evolution of ETL capabilities, together with extensibility features that support rapid development of new functions and workflows, will further democratize access to advanced analytics for non-experts while still offering depth for power users.
Crucially, the project’s open-source nature will continue to attract diverse contributions from individuals, startups, and major enterprises alike. A healthy, vibrant community that can communicate effectively across cultures and organizational boundaries will be essential to sustaining momentum. The balance between rapid iteration and production-grade reliability will persist as a central theme, guiding decisions about feature prioritization, testing, and governance. If the past decade serves as a guide, the next decade for Trino could bring broader adoption, richer capabilities, and new, unforeseen use cases that further demonstrate the engine’s value in enabling fast, data-driven decision-making.
Conclusion
The journey of Trino—from its origins within a single company’s analytics challenges to its current standing as a globally adopted, open-source distributed SQL engine—illustrates the enduring power of collaborative innovation. The project’s open-source lineage, the careful evolution of its architecture, and the expansion of its connector ecosystem have collectively shaped a platform that can meet the needs of modern enterprises seeking fast, scalable, and interoperable analytics across diverse data sources. The milestones reached over the past decade reflect not only technical prowess but also a community-driven commitment to making data analytics more accessible, faster, and more capable for organizations of all sizes.
As Trino continues to evolve, its capacity to unify disparate data stores, enable real-time insights, and support advanced data workflows will remain central to its value proposition. The ongoing emphasis on security, governance, and extensibility ensures that the platform can adapt to changing data landscapes without compromising reliability or performance. The future holds the promise of broader adoption, deeper capabilities, and an even more vibrant ecosystem that underpins enterprise analytics in an era where data-driven decisions are paramount.
