architecting-data

Name: architecting-data
Availability: InStock
Author: MaserMediaGroup

Design data platforms covering storage paradigms, modeling approaches, data mesh, and medallion architecture.

1|Updated Feb 24, 2026

Search Tags:#data modeling #dbt #data architecture #medallion architecture #lakehouse #data mesh #apache iceberg

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npx skills add https://github.com/masermediagroup-stack/maser-media --skill architecting-data-masermediagroup-stack

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: architecting-data
Source: https://github.com/masermediagroup-stack/maser-media/tree/main/.cursor/skills/community/ai-design-components/skills/architecting-data
Command: npx skills add https://github.com/masermediagroup-stack/maser-media --skill architecting-data-masermediagroup-stack

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Choosing the right data platform architecture is difficult: teams must decide between data lakes, warehouses, and lakehouses, pick modeling approaches, evaluate data mesh readiness, and select open table formats, often without a structured decision framework. ## Core Features & Use Cases - Decision Frameworks: Structured trees for storage paradigm selection, data modeling approach, data mesh readiness scoring, and open table format selection. - Architecture Patterns: Guidance on medallion architecture (bronze, silver, gold), dimensional modeling, Data Vault 2.0, wide tables, and governance with catalog, lineage, and quality checks. - Tool Recommendations: Research-validated comparisons of dbt, Apache Iceberg, Delta Lake, Snowflake, Databricks, Fivetran, Airbyte, and orchestration tools, with stack suggestions by organization size. - Use Case: A 200-person company evaluating data mesh can use the 6-factor readiness assessment to determine whether to decentralize or build a centralized foundation first. ## Quick Start Ask the architecting-data skill to recommend a data platform architecture for a startup using PostgreSQL, Stripe, and MongoDB with a small analytics team.

Frequently Asked Questions about architecting-data

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

FAQPage Schema

How do I choose between a data lake, data warehouse, and data lakehouse?▼

Choose a data warehouse for known BI workloads, a data lake for exploratory or ML use cases needing raw data, and a lakehouse for mixed BI and ML workloads with cost optimization. Lakehouses are typically 60-80% cheaper than warehouses at scale.

Apache Iceberg vs Delta Lake vs Hudi: which table format should I use?▼

Apache Iceberg is the default recommendation for new projects due to vendor neutrality and the broadest multi-engine support across Spark, Trino, and Flink. Choose Delta Lake if committed to Databricks, or Apache Hudi for CDC-heavy workloads with frequent upserts.

When should an organization adopt data mesh architecture?▼

Data mesh fits large organizations over 500 people with clear domain ownership, mature platform capabilities, and a central team that is a proven bottleneck. A 6-factor readiness assessment scoring 24-30 indicates a strong candidate; below 18, build foundations first.

What is medallion architecture and when should I use it?▼

Medallion architecture organizes lakehouse data into bronze (raw, immutable), silver (cleaned and deduplicated), and gold (business-level aggregates) layers. Use it on lakehouse platforms like Databricks or Iceberg when you want clear data quality layers and reprocessing at any stage.

Which data modeling approach fits BI versus ML workloads?▼

Dimensional modeling with star schemas fits BI dashboards with known query patterns, while wide denormalized tables suit ML feature stores and data science notebooks. Use normalized 3NF for OLTP systems and Data Vault 2.0 when compliance and full audit trails are required.

What data stack is recommended for a startup on a small budget?▼

A startup stack under $5K per month uses BigQuery or Snowflake for storage, Airbyte or Fivetran for ingestion, dbt Core for transformation, and Metabase or Looker Studio for visualization. This minimizes complexity by relying on managed services.