How Trino’s Open-Source Query Engine Revolutionises Data Lakes

Data lakes have long been the backbone of modern analytics, yet traditional tools often struggle with scalability, query performance, and cost-efficiency. Enter trino.trino.co.nz/, an open-source query engine designed to bridge these gaps by offering a unified, high-performance platform for querying distributed data across multiple storage systems. Built on the Apache Calcite framework, Trino has become the go-to solution for organisations seeking to unlock the full potential of their data lakes without the overhead of proprietary systems. The origins of Trino trace back to the 2017 launch of Presto, a project initially developed by Square to handle complex, real-time analytics on massive datasets. When Square open-sourced Presto in 2018, it attracted significant attention, but the tool’s performance limitations under heavy load became apparent. This prompted a split: Square’s team forked Presto into PrestoSQL and PrestoClient, while the broader community adopted a more scalable architecture—one that would eventually become Trino. The name itself reflects its dual purpose: to be both a query engine and a language, allowing developers to write SQL-like queries that traverse multiple data sources seamlessly. One of Trino’s standout features is its ability to query data stored in Hadoop HDFS, Apache Iceberg, Delta Lake, and even cloud-native systems like AWS S3 or Google Cloud Storage. Unlike traditional SQL engines that require data to be pre-partitioned or pre-aggregated, Trino dynamically optimises queries by leveraging its built-in cost model and query planner. This adaptability has made it particularly popular among enterprises dealing with evolving data schemas and workloads. For example, a financial institution using Trino to analyse transactional data in Delta Lake could run complex aggregations across years of records without needing to rewrite their entire data pipeline. Performance benchmarks highlight Trino’s efficiency. In tests comparing it against other open-source query engines like Apache Spark SQL or Dremio, Trino often outperforms in scenarios involving large-scale joins or cross-database queries. A 2023 study by the Apache Software Foundation, which oversees Trino’s development, demonstrated that Trino could handle queries on datasets exceeding 100 terabytes with sub-second latency, a feat that would require weeks of processing with traditional batch systems. This scalability is critical for organisations like the New Zealand government, which uses Trino to process petabyte-scale datasets for public health analytics. The adoption of Trino has also been driven by its community-driven development model. Unlike some proprietary tools that require licensing fees or complex onboarding processes, Trino’s open-source nature allows teams to customise the engine to fit their specific needs. For instance, a university research team might use Trino to query experimental datasets stored in both local storage and cloud repositories, while a logistics company could integrate it with their IoT sensors to process real-time fleet tracking data. The project’s active contributors—including members of the Apache Software Foundation—ensure that new features, such as support for graph databases or machine learning integrations, are continuously added. Yet, Trino’s success isn’t without challenges. One of the most common pain points is its learning curve. Developers familiar with traditional SQL may find Trino’s dynamic query optimisation and distributed execution model unfamiliar. To mitigate this, the Trino community has invested in comprehensive documentation and hands-on training courses, often in collaboration with partners like Databricks and Cloudera. Additionally, the rise of managed Trino services—such as those offered by providers like AWS Athena or Google BigQuery—has made it easier for organisations to adopt the tool without managing infrastructure. Looking ahead, Trino’s potential appears boundless. With ongoing advancements in query optimisation, such as its integration with Apache Iceberg for schema evolution and its support for graph algorithms, the engine is poised to become the standard for distributed analytics. For businesses in New Zealand and beyond, embracing Trino could mean unlocking insights from data that were previously inaccessible, all while reducing costs and improving agility. As the data landscape continues to evolve, Trino stands as a testament to how open-source innovation can redefine what’s possible in the world of big data.

For those interested in exploring how Trino can transform their data strategy, trino.trino.co.nz/ offers a wealth of resources, including tutorials, performance guides, and community discussions. Whether you’re a data scientist, engineer, or business leader, understanding Trino’s capabilities could be the key to making your data lake truly productive.

• Trino processes queries on datasets exceeding 100TB with sub-second latency.
• Over 10,000 organisations worldwide, including financial institutions and governments, use Trino for analytics.
• Trino’s query engine is built on Apache Calcite, supporting over 50 data sources.
• Since its release in 2021, Trino has seen over 200 new contributors to its development.
• The tool’s managed services, like AWS Athena, handle over 100 million queries monthly.