Community Showcase Part 2

2026-06-29 · Source: Dagster Blog · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This "Community Showcase Part 2" highlights two innovative projects built with Dagster by community members. Edwin Weber, an independent data engineer, developed a low-budget, open-source modern data stack project orchestrating Danish parliament data. His stack includes Dagster, dlt, dbt, DuckDB, Delta tables, and Metabase, processing data through Bronze, Silver, and Gold medallion layers. He ingeniously used Dagster assets to manage Metabase container states, preventing DuckDB locking conflicts. Separately, Parag Ekbote, an AI and Data Science undergraduate, created an open-source Python library integrating Dagster with Hugging Face Datasets. This library streamlines loading, processing, and pushing datasets to the Hugging Face Hub, featuring streaming support via a custom IO manager and comprehensive metadata management. Both projects underscore Dagster's flexibility and its asset-based architecture for diverse data engineering challenges.

Key takeaway

For data engineers building modern data stacks with open-source tools, consider Dagster for orchestration, especially if you use dbt or manage complex data assets. Its asset-based approach simplifies dependency management and allows creative solutions, like using assets to control external services to prevent resource conflicts. Explore its robust integrations and metadata capabilities for enhanced monitoring and reproducibility, particularly with large datasets or public data hubs.

Key insights

Dagster's asset-based architecture and robust integrations enable flexible, metadata-rich data orchestration for diverse, complex projects.

Principles

• Metadata-driven automation enhances data engineering.
• Asset-based orchestration simplifies complex data workflows.
• Open-source tools enable cost-effective, maintainable stacks.

Method

Use Dagster assets to manage external service states, like stopping/starting containers, to resolve resource conflicts in data pipelines. Implement custom IO managers for streaming large datasets and modules for comprehensive metadata extraction and publishing.

In practice

• Integrate dbt with Dagster for automatic dependency discovery.
• Use Dagster's UI for workflow visualization.
• Query Dagster's SQLite DB for DevOps dashboards.

Topics

• Dagster
• Data Orchestration
• Modern Data Stack
• dbt Integration
• Hugging Face Datasets
• Metadata Management
• DuckDB

Code references

• bgarcevic/danish-democracy-data
• edwinweber/dbt_duckdb_demo_public

Best for: Data Engineer, MLOps Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Dagster Blog.