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trino-lakehouse-platform-architect

Design Trino lakehouse architectures with Iceberg catalogs and Bronze/Silver/Gold modeling.

14|1|Updated May 5, 2026
Search Tags:#trino#iceberg#lakehouse#hive metastore#catalog design#minio s3#airflow dbt
One-click install
npx skills add https://github.com/ivanshamaev/de-agent-skills --skill trino-lakehouse-platform-architect
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trino-lakehouse-platform-architect
Source: https://github.com/ivanshamaev/de-agent-skills/tree/main/group_skills/trino_group_skills/trino_lakehouse_platform_architect
Command: npx skills add https://github.com/ivanshamaev/de-agent-skills --skill trino-lakehouse-platform-architect

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you design a production-ready Trino-based modern data platform that cleanly separates compute from storage while providing a coherent lakehouse architecture and metadata/catalog strategy.

Core Features & Use Cases

  • Trino query-layer architecture: define coordinator/worker topology and the principles for running Trino as a compute engine only.
  • Catalog and metadata-driven integration: design Iceberg catalogs via Hive Metastore or Glue, and connect operational sources (e.g., PostgreSQL) and streaming sources (e.g., Kafka).
  • Multi-layer lakehouse modeling: establish Bronze/Silver/Gold zones with appropriate Iceberg table settings, partitions, and performance-oriented options for analytics.
  • Federated query across heterogeneous sources: support cross-catalog querying while guiding trade-offs and anti-patterns.
  • Real-world example: model raw Kafka ingestions into Iceberg Bronze, transform and deduplicate into Silver using dbt, and publish curated aggregated facts into Gold for BI tools like Superset.

Quick Start

Ask your AI to draft a Trino lakehouse blueprint for a new platform on MinIO/S3 using Iceberg with Hive Metastore (or Glue), including coordinator/worker sizing guidance and Bronze/Silver/Gold table designs for Bronze raw events, Silver curated datasets, and Gold aggregated facts.

Frequently Asked Questions about trino-lakehouse-platform-architect

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a Trino lakehouse architecture that separates compute from storage?▼

Design a Trino lakehouse architecture separating compute from storage by configuring a coordinator/worker query topology and connecting it to object storage via Iceberg catalogs. This standardizes lakehouse metadata while keeping compute decoupled.

How do I choose between Hive Metastore and Glue for an Iceberg catalog?▼

Choosing between Hive Metastore and Glue for an Iceberg catalog depends on your existing platform, as both are supported options for standardizing lakehouse metadata and enabling federated access to your object storage and streaming sources.

How do I model Bronze Silver and Gold layers in an Iceberg lakehouse?▼

To model Bronze, Silver, and Gold layers in an Iceberg lakehouse, you ingest raw Kafka events into Iceberg Bronze tables, use dbt to transform and deduplicate into Silver, and publish curated aggregated facts into Gold for BI tools like Superset.

Can I run federated queries across Iceberg and PostgreSQL with Trino?▼

Yes, you can run federated queries across Iceberg and PostgreSQL with Trino by configuring cross-catalog querying, allowing you to join lakehouse data with operational sources while navigating the trade-offs and anti-patterns of heterogeneous source access.

What are the limitations of using Trino only as a compute engine for a lakehouse?▼

When using Trino only as a compute engine for a lakehouse, the main limitation is that it requires an external metadata catalog like Hive Metastore or Glue and separate object storage, meaning it cannot independently manage storage state or data ingestion.

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