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ShedBox Agent for Data Engineers

Automate pipeline creation and reduce maintenance overhead.

The Data Engineering Challenge​

Data engineers spend significant time on:

  • Writing repetitive ETL code
  • Managing connector maintenance
  • Debugging data quality issues
  • Handling stakeholder data requests

ShedBox Agent generates production-ready pipelines from natural language.

How Data Engineers Use ShedBox Agent​

Rapid Pipeline Generation​

You: "Create an ETL pipeline that:
- Pulls from Salesforce Opportunities API
- Joins with our PostgreSQL customers table
- Calculates win rates by segment
- Loads to our data warehouse"

ShedBox Agent:
✓ Generates complete pipeline YAML
✓ Handles API authentication
✓ Includes join logic
✓ Configures warehouse loading

Generated Pipeline​

data_sources:
  salesforce:
    type: rest_api
    url: https://${SF_INSTANCE}.salesforce.com/services/data/v57.0/query
    auth:
      type: oauth2
      token_url: https://login.salesforce.com/services/oauth2/token
      client_id_env: SF_CLIENT_ID
      client_secret_env: SF_CLIENT_SECRET
    params:
      q: "SELECT Id, AccountId, StageName, Amount, CloseDate FROM Opportunity"

  customers:
    type: postgresql
    connection_env: PROD_DATABASE_URL
    query: "SELECT id, segment, region FROM customers"

processing:
  join:
    sources: [salesforce, customers]
    left_key: AccountId
    right_key: id
    type: left

  aggregate:
    group_by: segment
    metrics:
      - total_opps: count
      - won_opps: count(StageName == 'Closed Won')
      - win_rate: won_opps / total_opps
      - total_value: sum(Amount)

output:
  type: postgresql
  connection_env: WAREHOUSE_URL
  table: sales_metrics
  mode: upsert
  key: segment

Key Benefits for Data Engineers​

1. Faster Development​

Generate pipeline scaffolding in seconds:

"Create a CDC pipeline for the orders table"
"Set up incremental loading from API X"
"Build a data quality validation pipeline"

2. Consistent Patterns​

Generated pipelines follow best practices:

  • Proper error handling
  • Idempotent operations
  • Incremental processing
  • Logging and monitoring hooks

3. Self-Service Enablement​

Empower stakeholders with supervised self-service:

# Stakeholder-friendly pipeline
# Generated by ShedBox Agent, reviewed by data engineering

metadata:
  owner: data-engineering
  reviewers: [jane@company.com]
  stakeholder: marketing-team
  refresh: daily

data_sources:
  marketing_data:
    type: rest_api
    url: https://api.hubspot.com/crm/v3/objects/contacts
    # ... config ...

4. Documentation Generation​

Auto-generate pipeline documentation:

"Document this pipeline with data lineage and transformation logic"

Data Engineering Workflows​

Pipeline Development​

"Create a template for incremental loading from any REST API"

Data Quality​

# Generated data quality checks
validation:
  - field: email
    rules:
      - not_null
      - format: email
  - field: amount
    rules:
      - not_null
      - type: numeric
      - range: [0, 1000000]

Debugging​

"This pipeline is duplicating records. Analyze and suggest fixes"

Migration​

"Generate a migration plan from Airflow to ShedBoxAI"

Production Patterns​

Orchestration​

pipelines:
  - name: extract_salesforce
    schedule: "0 */2 * * *"
    timeout: 30m

  - name: transform_data
    depends_on: [extract_salesforce]

  - name: load_warehouse
    depends_on: [transform_data]

Monitoring​

monitoring:
  metrics:
    - records_processed
    - execution_time
    - error_count

  alerts:
    - condition: error_count > 0
      notify: pagerduty
    - condition: execution_time > 30m
      notify: slack

Testing​

tests:
  - name: validate_output_schema
    type: schema
    expected:
      - name: segment
        type: string
      - name: win_rate
        type: float

  - name: validate_row_count
    type: assertion
    query: "SELECT COUNT(*) FROM sales_metrics"
    condition: "> 0"

Get Started​

Accelerate your data engineering with AI-generated pipelines.

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