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Trino: Syllabus
Mastering Trino: High-Performance SQL on Data Lakes with MinIO, Delta Lake, Iceberg & Grafana
Trino (formerly PrestoSQL) is an open-source distributed SQL query engine designed for fast analytics on large datasets. With support for data lakes, federated queries, and real-time analytics, Trino enables enterprises to run SQL queries across multiple data sources efficiently. This book provides a hands-on, implementation-first approach to mastering Trino, integrating it with MinIO, Delta Lake, Iceberg, and Grafana.
Module 1: Introduction to Trino and Distributed SQL Processing
- Understanding Trino’s architecture and query execution model
- Comparison with traditional databases and data warehouses
- Installing Trino and setting up a standalone environment
- Configuring Trino for high availability and scalability
Module 2: Trino and Data Lake Integration
- Understanding data lake architectures and their challenges
- Connecting Trino to object storage with MinIO
- Querying structured and unstructured data with Trino
- Configuring Trino catalogs for Delta Lake and Apache Iceberg
Module 3: Trino Querying and SQL Optimization
- Writing SQL queries in Trino: SELECT, JOIN, GROUP BY, HAVING
- Using window functions and complex aggregations
- Performance tuning and query optimization techniques
- Understanding Trino’s cost-based optimizer
Module 4: Federated Queries and Multi-Source Analytics
- Querying multiple data sources with Trino
- Connecting Trino to MySQL, PostgreSQL, and MongoDB
- Using Trino for cross-database joins and aggregations
- Data virtualization and real-time federated queries
Module 5: Trino and Data Warehousing
- Using Trino as a query engine for modern data warehouses
- Integrating Trino with Apache Hive Metastore
- Querying Parquet, ORC, and Avro files efficiently
- Comparing Trino with Snowflake and BigQuery
Module 6: Trino Performance Tuning and Scaling
- Configuring worker nodes and query coordinators
- Caching strategies for faster query execution
- Resource allocation and workload management
- Scaling Trino clusters in Kubernetes and cloud environments
Module 7: Trino and Apache Iceberg
- Querying Iceberg tables with Trino
- Understanding Iceberg metadata and snapshot-based querying
- Schema evolution and time travel with Trino and Iceberg
- Optimizing Iceberg queries for large datasets
Module 8: Trino and Delta Lake
- Using Trino for querying Delta Lake tables
- Implementing ACID transactions with Delta Lake
- Time travel queries and versioned datasets
- Best practices for Delta Lake and Trino integration
Module 9: Real-Time Analytics and Streaming with Trino
- Querying real-time event streams with Apache Kafka and Trino
- Using Trino for streaming ETL and log analytics
- Implementing Change Data Capture (CDC) workflows with Trino
- Analyzing time-series data in real time
Module 10: Security and Access Control in Trino
- Implementing role-based access control (RBAC) in Trino
- Securing queries and data access with TLS and authentication
- Integrating Trino with Apache Ranger and LDAP
- Auditing query logs and user activity tracking
Module 11: Monitoring and Observability with Trino and Grafana
- Setting up query monitoring and performance dashboards
- Integrating Trino with Prometheus for real-time metrics
- Building interactive visualizations with Grafana
- Analyzing query execution plans and optimizing workloads
Module 12: Deploying Trino in Production
- Running Trino in Kubernetes with Helm charts
- Deploying Trino on AWS, Azure, and GCP
- Managing multi-cluster deployments and auto-scaling
- Implementing CI/CD pipelines for Trino SQL workflows
Hands-On Projects
Project 1: Building a Unified SQL Query Engine with Trino and MinIO
- Set up a Trino cluster with MinIO as the object storage backend
- Create and manage catalogs for structured and semi-structured data
- Optimize query performance using caching and partitioning
Project 2: Real-Time Data Analytics with Trino and Apache Kafka
- Stream data from Kafka into Trino for real-time analytics
- Implement continuous ETL workflows for data transformation
- Optimize streaming queries for low-latency analytics
Project 3: Querying Versioned Datasets with Trino, Delta Lake, and Iceberg
- Configure Trino to read and query Iceberg and Delta Lake tables
- Implement time travel queries for historical data analysis
- Use schema evolution to handle dynamic data changes
Project 4: Interactive Data Dashboards with Trino and Grafana
- Connect Trino to Grafana for live query visualization
- Build dashboards for monitoring key business metrics
- Implement alerting and anomaly detection with Prometheus
Project 5: Secure Multi-Tenant Data Lake with Trino, MinIO, and Iceberg
- Implement role-based access control for Trino queries
- Set up multi-tenant object storage with MinIO and IAM policies
- Deploy and monitor a scalable Trino data lakehouse in production