ShedBoxAI vs Dagster

Dagster introduced software-defined assets for data orchestration. ShedBoxAI offers a simpler YAML-based alternative.

Quick Comparison​

Key Differences​

Software-Defined Assets vs YAML​

Dagster organizes pipelines around assets:

from dagster import asset

@asset
def customers():
    return load_customers()

@asset
def active_customers(customers):
    return customers[customers.status == 'active']

@asset
def customer_report(active_customers):
    return generate_report(active_customers)

ShedBoxAI focuses on data flow:

data_sources:
  customers:
    type: csv
    path: "customers.csv"

processing:
  contextual_filtering:
    customers:
      - field: "status"
        condition: "active"
        new_name: "active_customers"

output:
  type: file
  path: "customer_report.json"
  format: json

Dagit UI vs CLI​

Dagster includes Dagit, a web UI for monitoring. ShedBoxAI is CLI-first with Claude Code integration for AI-assisted configuration.

When to Choose Dagster​

• You think in terms of data assets
• You need the Dagit UI for monitoring
• Your team prefers Python-native tools
• You need complex asset dependencies

When to Choose ShedBoxAI​

• You want the simplest possible setup
• You need built-in AI integration
• You prefer declarative configuration
• You want quick iterations

Get Started​

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