ShedBoxAI vs dbt

dbt revolutionized SQL-based data transformation. ShedBoxAI offers a broader pipeline framework that includes extraction, AI processing, and more.

Quick Comparison​

Key Differences​

Scope of Work​

dbt focuses on transforming data already in your warehouse:

-- models/active_customers.sql
SELECT *
FROM {{ ref('raw_customers') }}
WHERE status = 'active'

ShedBoxAI handles the full pipeline including extraction:

data_sources:
  customers:
    type: rest
    url: "https://api.example.com/customers"
    headers:
      Authorization: "Bearer ${API_TOKEN}"
    response_path: "data"

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

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

AI Processing​

ShedBoxAI includes native AI capabilities:

ai_interface:
  model:
    type: rest
    url: "https://api.anthropic.com/v1/messages"
    method: POST
    headers:
      x-api-key: "${ANTHROPIC_API_KEY}"
      Content-Type: "application/json"
    options:
      model: "claude-sonnet-4-20250514"

  prompts:
    sentiment:
      user_template: |
        Analyze sentiment:
        {% for item in feedback %}
        - {{ item.text }}
        {% endfor %}

dbt requires external integrations for AI processing.

Complementary Tools​

ShedBoxAI and dbt can work together:

• Use ShedBoxAI for extraction and AI processing
• Use dbt for SQL transformations in your warehouse

When to Choose dbt​

• You work primarily with SQL transformations
• Your data is already in a data warehouse
• You need dbt's testing and documentation features
• Your team thinks in SQL

When to Choose ShedBoxAI​

• You need to extract data from external sources
• You want built-in AI processing
• You prefer a single tool for the full pipeline
• You're not warehouse-centric

Get Started​

• Quick Start Guide
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