Distributed SQL Data Engineering with Trino Training Course  

Course Introduction

Distributed SQL Data Engineering with Trino Training Course provides an in-depth practical and architectural foundation for using Trino to query, integrate, transform, and analyze data distributed across heterogeneous enterprise data platforms. The course explores Trino’s distributed query engine, connector architecture, catalogs, schemas, query execution model, SQL capabilities, and federation patterns, enabling participants to develop modern data solutions without unnecessarily moving all data into a single centralized repository.

The course examines how Trino enables organizations to create a logical analytical access layer across data lakes, object storage, relational databases, cloud warehouses, NoSQL platforms, and other enterprise data sources. Participants learn how catalogs and connectors expose diverse data systems through a consistent SQL interface while considering differences in data formats, storage models, transactional behavior, schemas, security, and performance. The programme emphasizes practical approaches for building governed and maintainable distributed SQL environments.

Participants develop advanced skills in distributed SQL querying, joins, aggregations, filtering, common table expressions, window functions, subqueries, analytical functions, and data transformation. Particular attention is given to query planning and execution, predicate and projection pushdown, partition pruning, dynamic filtering, data locality, exchange operations, parallel execution, and resource management. These concepts enable engineers and architects to understand why distributed queries behave differently from traditional single-database SQL workloads and how to engineer them effectively.

The training also addresses Trino-based data engineering workflows involving data lakes, lakehouse architectures, open table formats, and cloud-native storage. Participants explore integration patterns involving technologies such as Hive-compatible environments, Iceberg, Delta Lake, relational databases, object stores, Kafka, and cloud data platforms. The course considers how distributed SQL can support data virtualization, federated analytics, interactive exploration, ELT, data migration, data quality workflows, and governed access to multiple analytical domains.

Advanced operational topics include Trino cluster architecture, worker and coordinator roles, fault tolerance, workload management, access control, authentication, encryption, observability, query monitoring, performance diagnostics, capacity planning, and production troubleshooting. Emerging issues such as data mesh, data products, lakehouse modernization, real-time analytics, AI and machine learning workloads, federated governance, semantic layers, and increasingly heterogeneous cloud data estates are examined to help participants understand how Trino can fit into evolving enterprise data strategies.

By the end of the programme, participants will be able to design, implement, optimize, secure, and operate distributed SQL data engineering solutions using Trino. The course combines SQL proficiency, distributed systems concepts, platform architecture, data integration, performance engineering, governance, and operational practices so that organizations can improve access to distributed data while reducing unnecessary duplication, supporting analytical agility, and creating a scalable foundation for modern enterprise data engineering.

Duration

10 days

Who Should Attend

• Data Engineers responsible for building distributed data integration, transformation, and analytical processing solutions.

• Senior Data Engineers seeking advanced expertise in Trino and distributed SQL engineering.

• Data Architects designing modern data lake, lakehouse, and federated analytics architectures.

• Cloud Data Engineers integrating heterogeneous cloud storage, databases, warehouses, and analytical platforms.

• Analytics Engineers developing reusable SQL-based transformation and analytical workflows across multiple data sources.

• Big Data Engineers working with distributed query engines, data lakes, and large-scale analytical environments.

• Database Engineers responsible for integrating traditional relational platforms with modern analytical architectures.

• Data Platform Engineers managing Trino clusters, connectors, catalogs, security, performance, and operational standards.

• Data Warehouse Professionals transitioning from centralized warehouse architectures toward distributed data platforms.

• Business Intelligence Engineers requiring governed SQL access across multiple enterprise data repositories.

• Data Scientists requiring scalable analytical access to distributed and heterogeneous datasets.

• Machine Learning Engineers preparing distributed data for model development, feature engineering, and analytical workloads.

• Solution Architects evaluating distributed SQL technologies for enterprise data modernization initiatives.

• Enterprise Architects designing federated, hybrid-cloud, and multi-platform data architectures.

• DevOps and Platform Operations professionals supporting production Trino environments and data infrastructure.

• Data Governance Professionals addressing access control, metadata, lineage, compliance, and federated data management.

• Technical Leads responsible for data platform standards, architecture decisions, engineering practices, and modernization programmes.

• IT Managers overseeing enterprise analytics platforms, data engineering teams, cloud migration, and distributed data initiatives.

Course Objectives

• Develop a comprehensive understanding of Trino architecture, distributed SQL processing, coordinator and worker roles, connectors, catalogs, schemas, and query execution.

• Design distributed SQL solutions that provide consistent analytical access to heterogeneous data sources without requiring unnecessary physical data consolidation.

• Apply advanced SQL techniques for joins, aggregations, window functions, subqueries, common table expressions, analytical transformations, and complex distributed workloads.

• Configure and use Trino catalogs and connectors to integrate relational databases, data lakes, object storage, cloud warehouses, streaming platforms, and other enterprise data systems.

• Analyze distributed query execution plans and identify opportunities to improve performance through predicate pushdown, projection pushdown, partition pruning, dynamic filtering, and optimized joins.

• Design efficient data lake and lakehouse architectures using Trino with open table formats, cloud object storage, metadata catalogs, and modern analytical platforms.

• Implement data engineering workflows for ELT, federation, data virtualization, analytical integration, migration, data quality validation, and cross-platform data exploration.

• Apply workload management, resource controls, concurrency strategies, memory management, and capacity planning techniques to maintain predictable distributed query performance.

• Establish secure Trino environments using authentication, authorization, access controls, encryption, credential management, auditing, and enterprise security practices.

• Implement monitoring and observability practices that provide visibility into query performance, cluster health, resource utilization, connector behavior, failures, and operational trends.

• Evaluate Trino architecture for emerging data engineering patterns involving data mesh, data products, real-time analytics, AI workloads, lakehouse modernization, and hybrid-cloud platforms.

• Develop an enterprise implementation and operating strategy for Trino that aligns technical architecture, governance, performance, security, scalability, and long-term platform sustainability.

Comprehensive Course Outline

Module 1: Foundations of Trino and Distributed SQL

• Understanding Trino’s role as a distributed SQL query engine and its position within modern enterprise data engineering architectures.

• Examining distributed SQL principles, parallel query processing, data locality, network exchanges, and the differences from traditional database execution.

• Exploring common Trino use cases including federated analytics, data virtualization, lakehouse querying, ELT, migration, and cross-platform analytical access.

• Identifying architectural scenarios where distributed SQL provides value while recognizing workload, governance, latency, and operational considerations.

Module 2: Trino Architecture and Cluster Components

• Understanding coordinator and worker responsibilities, query submission, planning, scheduling, task execution, and distributed resource utilization.

• Examining Trino’s query lifecycle from SQL submission and parsing through optimization, stage creation, task execution, and result delivery.

• Configuring cluster topology and understanding how worker capacity, network connectivity, memory, and CPU resources influence distributed execution.

• Designing production-oriented Trino clusters with appropriate availability, scalability, isolation, monitoring, and operational management practices.

Module 3: SQL Engineering with Trino

• Applying Trino SQL for filtering, projection, aggregation, sorting, grouping, joins, subqueries, common table expressions, and complex analytical transformations.

• Using advanced SQL capabilities including window functions, conditional expressions, date and time functions, arrays, maps, JSON processing, and string operations.

• Designing reusable SQL transformations that remain understandable, maintainable, testable, and efficient across large distributed datasets.

• Adapting conventional database SQL practices to Trino while accounting for distributed execution, connector capabilities, and data movement considerations.

Module 4: Catalogs, Schemas and Connector Architecture

• Understanding catalogs, schemas, tables, views, connector configuration, and the abstraction layers through which Trino accesses external data systems.

• Configuring connectors for heterogeneous platforms while accounting for source-specific capabilities, authentication requirements, data types, and transactional behavior.

• Designing catalog structures that provide clear ownership, discoverability, security boundaries, and consistent access patterns across enterprise data domains.

• Managing connector lifecycle, configuration standards, compatibility, versioning, and operational dependencies in large-scale Trino environments.

Module 5: Data Lake and Object Storage Integration

• Querying large-scale datasets stored in cloud object storage and data lake environments using efficient distributed SQL access patterns.

• Working with common file formats such as Parquet, ORC, Avro, and JSON while understanding their impact on storage efficiency and query performance.

• Applying partitioning, file sizing, compression, columnar storage, statistics, and metadata strategies to improve data lake query efficiency.

• Designing scalable data lake access architectures that balance flexibility, performance, governance, storage costs, and analytical requirements.

Module 6: Lakehouse Architecture and Open Table Formats

• Understanding lakehouse principles and how Trino can provide SQL-based access to transactional analytical tables stored on open data lake infrastructure.

• Working with open table formats such as Apache Iceberg and other supported lakehouse technologies for scalable analytical data management.

• Designing table partitioning, schema evolution, snapshots, metadata management, and lifecycle strategies for modern analytical environments.

• Applying Trino to lakehouse workloads involving interactive analytics, ELT, data products, historical analysis, and cross-domain data consumption.

Module 7: Federated Querying and Cross-Platform Integration

• Designing queries that combine information from relational databases, data lakes, cloud warehouses, SaaS platforms, and other heterogeneous sources.

• Understanding distributed joins across connectors and evaluating data movement, network traffic, source-system impact, and query execution implications.

• Applying federation patterns for analytical integration while maintaining appropriate governance, source-system protection, security, and performance controls.

• Establishing architectural criteria for deciding when to federate queries and when to materialize, replicate, cache, or transform data into analytical stores.

Module 8: Distributed Query Optimization and Performance Engineering

• Reading and interpreting Trino query execution plans to identify expensive stages, excessive data movement, inefficient joins, and resource-intensive operations.

• Applying predicate pushdown, projection pushdown, partition pruning, dynamic filtering, join optimization, and statistics to reduce unnecessary processing.

• Managing data skew, small files, inefficient partitioning, high-cardinality operations, and excessive network exchange within distributed workloads.

• Establishing repeatable performance engineering practices using benchmarking, query profiling, workload analysis, capacity planning, and controlled optimization.

Module 9: Data Engineering and ELT with Trino

• Designing SQL-driven ELT workflows that extract, transform, validate, aggregate, and prepare distributed data for analytical consumption.

• Implementing transformations across multiple source systems while maintaining appropriate data quality, lineage, dependency, and operational controls.

• Developing reusable SQL pipelines for dimensional models, analytical datasets, curated data products, reporting structures, and downstream applications.

• Integrating Trino with orchestration and automation platforms to create repeatable, observable, and production-ready data engineering workflows.

Module 10: Workload Management, Resource Governance and Scalability

• Understanding Trino resource groups, workload classification, concurrency management, memory limits, CPU utilization, and workload isolation principles.

• Designing resource governance strategies that protect critical workloads from unpredictable query behavior and excessive resource consumption.

• Applying capacity planning techniques to estimate cluster requirements based on data volume, query complexity, concurrency, latency expectations, and growth.

• Scaling Trino environments while maintaining predictable performance, operational stability, cost efficiency, and appropriate separation of competing workloads.

Module 11: Security, Access Control and Data Governance

• Implementing authentication, authorization, role-based access controls, catalog permissions, schema permissions, table privileges, and enterprise security policies.

• Protecting sensitive and regulated data through encryption, credential management, secure connectivity, masking strategies, and controlled data exposure.

• Integrating Trino with enterprise identity providers and security architectures while maintaining auditable access to distributed data resources.

• Establishing governance practices covering data ownership, metadata, lineage, classification, retention, auditability, and responsible federated data access.

Module 12: Observability, Monitoring and Troubleshooting

• Monitoring coordinator and worker health, query activity, memory utilization, CPU consumption, network traffic, task execution, and connector behavior.

• Analyzing query failures, memory errors, timeouts, resource contention, connectivity issues, slow stages, and unexpected execution behavior.

• Designing operational dashboards, alerts, logs, metrics, and diagnostic procedures that support proactive management of production Trino environments.

• Developing incident response and troubleshooting practices that enable engineering teams to identify root causes and restore reliable distributed query operations.

Module 13: High Availability, Resilience and Production Operations

• Designing resilient Trino deployments that address coordinator availability, worker failures, network disruptions, dependency failures, and infrastructure interruptions.

• Understanding fault-tolerant execution capabilities and evaluating recovery approaches for long-running and business-critical distributed workloads.

• Establishing production operational procedures covering upgrades, configuration changes, capacity expansion, maintenance, backup considerations, and controlled deployments.

• Developing service-level practices for Trino platforms, including availability expectations, incident management, operational ownership, and performance baselines.

Module 14: Trino in Cloud, Hybrid and Multi-Platform Environments

• Designing Trino architectures for public cloud, private infrastructure, hybrid environments, and multi-platform enterprise data estates.

• Integrating cloud object storage, managed databases, cloud warehouses, Kubernetes environments, identity services, networking, and enterprise observability platforms.

• Evaluating architectural trade-offs involving compute separation, storage locality, network costs, elasticity, security boundaries, and regional data placement.

• Establishing deployment and operating standards that support portability, automation, infrastructure-as-code, controlled scaling, and consistent enterprise management.

Module 15: Advanced Data Architecture and Emerging Use Cases

• Applying Trino within data mesh architectures where distributed domain data is exposed as governed, discoverable, reusable analytical data products.

• Supporting real-time and near-real-time analytics by combining streaming-aware platforms with distributed SQL access to operational and analytical datasets.

• Exploring Trino’s role in AI and machine learning data preparation, feature engineering, model development datasets, and analytical workflows supporting generative AI.

• Evaluating emerging lakehouse, semantic layer, data virtualization, intelligent automation, and federated analytics patterns within modern enterprise data architectures.

Module 16: Enterprise Trino Strategy, Implementation and Future Readiness

• Developing an enterprise Trino adoption strategy covering architecture, platform standards, use-case prioritization, governance, security, skills, and operating models.

• Establishing implementation roadmaps for migrating from fragmented analytical access patterns toward governed distributed SQL and modern data platform architectures.

• Defining standards for SQL engineering, connector management, performance optimization, workload governance, observability, security, and platform lifecycle management.

• Preparing Trino environments for future data growth, expanding cloud estates, AI-enabled workloads, real-time analytics, data products, and increasingly heterogeneous enterprise data ecosystems.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.