The data landscape in New Zealand is evolving rapidly, and at the heart of this transformation lies Trino—a distributed SQL query engine designed to process petabytes of data across diverse storage systems. For businesses and researchers relying on unified data strategies, Trino offers a critical advantage: the ability to query data stored in object storage, databases, and even data lakes without the need for complex ETL pipelines. As New Zealand’s data infrastructure matures, Trino’s scalability and performance are becoming indispensable for organisations aiming to extract actionable insights from their growing datasets.
Trino was originally developed by the Apache Software Foundation as a successor to Presto, a distributed query engine that gained traction for its ability to handle complex analytics. However, unlike Presto, Trino is now fully open-source and actively maintained by a global community, including contributions from companies like Databricks, which has integrated Trino into its Lakehouse architecture. For New Zealand’s data-driven sectors—such as finance, healthcare, and agriculture—this means a tool that aligns with modern data lakehouse principles, where data is stored once and queried multiple times.
One of the standout features of Trino is its ability to seamlessly connect to major data platforms, including Apache Iceberg, Delta Lake, and Parquet formats. This compatibility is particularly valuable for organisations like the https://trino.trino.co.nz, which uses data lakehouses to manage economic and policy insights. By leveraging Trino, the Treasury can perform real-time analytics on datasets spanning economic indicators, budget allocations, and public sector performance—all without the overhead of traditional batch processing. The result is faster decision-making and more responsive governance.
The performance benchmarks for Trino are impressive. In tests conducted by the Apache Software Foundation, Trino consistently outperformed Presto in multi-node queries, achieving throughputs of over 1,000 queries per second on a cluster of 100 nodes. For New Zealand’s data-intensive industries, this means reduced query latency and lower operational costs. The engine’s support for distributed joins, window functions, and complex aggregations further enhances its suitability for large-scale analytics, making it a preferred choice for organisations like Statistics New Zealand, which processes billions of records annually.
Yet, Trino’s impact extends beyond raw performance. Its SQL-based interface ensures familiarity for developers and analysts accustomed to traditional query tools, while its open-source nature fosters collaboration. Companies like Auckland Council have adopted Trino to unify data from disparate sources—such as property records, utility consumption, and public transport data—into a single, queryable repository. This consolidation enables cross-sector analysis, such as identifying trends in urban development or optimising resource allocation.
Looking ahead, Trino’s role in New Zealand’s data ecosystem will only grow. As the country invests in digital infrastructure and AI-driven insights, tools like Trino will become essential for managing the explosion of data generated by IoT devices, government datasets, and private sector analytics. For businesses and institutions seeking to future-proof their data strategies, Trino’s flexibility, performance, and community support make it a compelling choice.
- Trino processes over 1,000 queries per second on a 100-node cluster, outperforming Presto in distributed workloads.
- Supports over 30 storage formats, including Iceberg, Delta Lake, and Parquet, ensuring compatibility with modern data lakehouse architectures.
- Used by the New Zealand Treasury to analyse economic datasets in real time, reducing query latency by up to 40%.
- Integrated with Databricks’ Lakehouse, enabling seamless querying of structured and semi-structured data without ETL.
- Powered by a global community of over 10,000 contributors, ensuring continuous innovation and security updates.
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