Data Processing ETL

Basic ETL Pattern

YAML
TypeScript

ensemble: etl-pipeline

agents:
  # Extract
  - name: extract
    operation: http
    config:
      url: https://source-api.example.com/data

  # Transform
  - name: transform
    operation: code
    inputs:
      data: ${extract.output.body}
      template: ${[email protected]}

  # Validate
  - name: validate
    operation: validate
    inputs:
      data: ${transform.output}
      schema: ${[email protected]}

  # Load
  - name: load
    condition: ${validate.output.valid}
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: INSERT INTO target_table ...
      params: ${validate.output.data}

import { createEnsemble, step, branch } from '@anthropic/conductor'

const etlPipeline = createEnsemble('etl-pipeline')
  // Extract
  .addStep(
    step('extract')
      .operation('http')
      .config({ url: 'https://source-api.example.com/data' })
  )
  // Transform
  .addStep(
    step('transform')
      .operation('code')
      .input({
        data: '${extract.output.body}',
        template: '${[email protected]}'
      })
  )
  // Validate
  .addStep(
    step('validate')
      .operation('validate')
      .input({
        data: '${transform.output}',
        schema: '${[email protected]}'
      })
  )
  // Load (conditional)
  .addStep(
    branch('load-if-valid')
      .condition('${validate.output.valid}')
      .then(
        step('load')
          .operation('data')
          .config({
            backend: 'd1',
            binding: 'DB',
            operation: 'execute',
            sql: 'INSERT INTO target_table ...',
            params: '${validate.output.data}'
          })
      )
  )
  .build()

export default etlPipeline

Database to Database

ensemble: db-to-db-etl

agents:
  # Extract from source
  - name: extract
    operation: data
    config:
      backend: d1
      binding: DB
      operation: query
      sql: |
        SELECT *
        FROM source_table
        WHERE updated_at > ?
        ORDER BY id
        LIMIT 1000
      params: [${input.last_sync_time}]

  # Transform each record
  - name: transform
    operation: code
    inputs:
      data: ${extract.output}
      template:
        - id: ${item.source_id}
          name: ${uppercase(item.name)}
          email: ${lowercase(item.email)}
          created: ${formatDate(item.created_at)}
          metadata:
            source: legacy_db
            migrated_at: ${Date.now()}
      functions:
        uppercase: (str) => str.toUpperCase()
        lowercase: (str) => str.toLowerCase()
        formatDate: (date) => new Date(date).toISOString()

  # Validate transformed data
  - name: validate
    operation: validate
    inputs:
      data: ${transform.output}
      schema:
        id: string
        name: string
        email:
          type: string
          pattern: ^[a-z0-9._%+-]+@[a-z0-9.-]+\.[a-z]{2,}$
        created: string

  # Handle validation errors
  - name: log-errors
    condition: ${!validate.output.valid}
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: INSERT INTO etl_errors (batch_id, errors, timestamp) VALUES (?, ?, ?)
      params:
        - ${input.batch_id}
        - ${JSON.stringify(validate.output.errors)}
        - ${Date.now()}

  # Load valid records
  - name: load
    condition: ${validate.output.valid}
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: |
        INSERT OR REPLACE INTO target_table (id, name, email, created, metadata)
        VALUES (?, ?, ?, ?, ?)
      params: ${validate.output.data.map(r => [
        r.id, r.name, r.email, r.created, JSON.stringify(r.metadata)
      ])}

  # Update sync timestamp
  - name: update-sync
    condition: ${load.success}
    operation: storage
    config:
      type: kv
      action: put
      key: last_sync_time
      value: ${Date.now()}

output:
  records_processed: ${extract.output.length}
  records_loaded: ${load.success ? validate.output.data.length : 0}
  errors: ${validate.output.valid ? [] : validate.output.errors}

API to Data Warehouse

ensemble: api-to-warehouse

agents:
  # Extract from multiple API endpoints (parallel)
  - name: fetch-users
    operation: fetch
    inputs:
      url: https://api.example.com/users
      cacheTTL: 300

  - name: fetch-orders
    operation: fetch
    inputs:
      url: https://api.example.com/orders
      cacheTTL: 300

  - name: fetch-products
    operation: fetch
    inputs:
      url: https://api.example.com/products
      cacheTTL: 300

  # Transform users
  - name: transform-users
    operation: code
    inputs:
      data: ${fetch-users.output.body}
      template:
        - user_id: ${item.id}
          full_name: ${item.firstName} ${item.lastName}
          email: ${item.email}
          registration_date: ${item.createdAt}
          lifetime_value: ${item.orders.reduce((sum, o) => sum + o.total, 0)}

  # Transform orders
  - name: transform-orders
    operation: code
    inputs:
      data: ${fetch-orders.output.body}
      template:
        - order_id: ${item.id}
          user_id: ${item.userId}
          total: ${item.total}
          items_count: ${item.items.length}
          order_date: ${item.createdAt}
          status: ${item.status}

  # Transform products
  - name: transform-products
    operation: code
    inputs:
      data: ${fetch-products.output.body}
      template:
        - product_id: ${item.id}
          name: ${item.name}
          category: ${item.category}
          price: ${item.price}
          stock: ${item.stock}

  # Load to data warehouse (parallel)
  - name: load-users
    operation: http
    config:
      url: https://warehouse.example.com/api/load
      method: POST
      body:
        table: users
        data: ${transform-users.output}

  - name: load-orders
    operation: http
    config:
      url: https://warehouse.example.com/api/load
      method: POST
      body:
        table: orders
        data: ${transform-orders.output}

  - name: load-products
    operation: http
    config:
      url: https://warehouse.example.com/api/load
      method: POST
      body:
        table: products
        data: ${transform-products.output}

  # Log pipeline execution
  - name: log
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: |
        INSERT INTO etl_log (
          pipeline, users_loaded, orders_loaded, products_loaded, timestamp
        ) VALUES (?, ?, ?, ?, ?)
      params:
        - api-to-warehouse
        - ${transform-users.output.length}
        - ${transform-orders.output.length}
        - ${transform-products.output.length}
        - ${Date.now()}

CSV Import with Validation

ensemble: csv-import

agents:
  # Fetch CSV from R2
  - name: fetch-csv
    operation: storage
    config:
      type: r2
      action: get
      key: imports/${input.file_name}

  # Parse CSV
  - name: parse
    operation: code
    config:
      script: scripts/parse-csv
    input:
      csv: ${fetch-csv.output}

  # Validate each record
  - name: validate
    operation: validate
    inputs:
      data: ${parse.output.records}
      schema:
        name:
          type: string
          required: true
        email:
          type: string
          pattern: ^[a-z0-9._%+-]+@[a-z0-9.-]+\.[a-z]{2,}$
          required: true
        age:
          type: number
          min: 0
          max: 120

  # Split valid and invalid
  - name: split-records
    operation: code
    config:
      script: scripts/split-validated-records
    input:
      validation: ${validate.output}
      records: ${parse.output.records}

  # Load valid records
  - name: load-valid
    condition: ${split-records.output.valid.length > 0}
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: INSERT INTO users (name, email, age) VALUES (?, ?, ?)
      params: ${split-records.output.valid.map(r => [r.name, r.email, r.age])}

  # Store invalid records
  - name: store-invalid
    condition: ${split-records.output.invalid.length > 0}
    operation: storage
    config:
      type: r2
      action: put
      key: imports/invalid/${input.file_name}
      value: ${JSON.stringify(split-records.output.invalid)}

  # Notify completion
  - name: notify
    operation: email
    config:
      to: ${input.uploader_email}
      subject: "CSV Import Complete - ${input.file_name}"
      body: |
        Import Results:
        - Valid records: ${split-records.output.valid.length}
        - Invalid records: ${split-records.output.invalid.length}
        - Total processed: ${parse.output.records.length}

        ${split-records.output.invalid.length > 0 ?
          'Invalid records saved to: imports/invalid/' + input.file_name : ''}

// scripts/parse-csv.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function parseCsv(context: AgentExecutionContext) {
  const { csv } = context.input
  const lines = csv.split('\n')
  const headers = lines[0].split(',')

  const records = lines.slice(1).map(line => {
    const values = line.split(',')
    return Object.fromEntries(
      headers.map((h, i) => [h, values[i]])
    )
  })

  return { records }
}

// scripts/split-validated-records.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function splitValidatedRecords(context: AgentExecutionContext) {
  const { validation, records } = context.input

  if (validation.valid) {
    return {
      valid: validation.data,
      invalid: []
    }
  }

  // Split records by validation errors
  const valid = []
  const invalid = []

  records.forEach((record, idx) => {
    const error = validation.errors.find(e => e.row === idx)
    if (error) {
      invalid.push({ ...record, error: error.message })
    } else {
      valid.push(record)
    }
  })

  return { valid, invalid }
}

// scripts/return-no-new-data.ts
import type { AgentExecutionContext } from '@ensemble-edge/conductor'

export default function returnNoNewData(context: AgentExecutionContext) {
  return { message: 'No new data' }
}

Incremental Sync

ensemble: incremental-sync

state:
  schema:
    last_sync_id: number
    last_sync_time: string

agents:
  # Get last sync point
  - name: get-checkpoint
    operation: storage
    state:
      use: [last_sync_id, last_sync_time]
    config:
      type: kv
      action: get
      key: sync_checkpoint

  # Extract new/updated records
  - name: extract
    operation: data
    config:
      backend: d1
      binding: DB
      operation: query
      sql: |
        SELECT *
        FROM source_table
        WHERE id > ? OR updated_at > ?
        ORDER BY id
        LIMIT 10000
      params:
        - ${state.last_sync_id || 0}
        - ${state.last_sync_time || '1970-01-01'}

  # No new data? Skip rest
  - name: check-empty
    condition: ${extract.output.length === 0}
    operation: code
    config:
      script: scripts/return-no-new-data

  # Transform
  - name: transform
    condition: ${extract.output.length > 0}
    operation: code
    inputs:
      data: ${extract.output}
      template: ${[email protected]}

  # Load
  - name: load
    condition: ${transform.executed}
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: INSERT OR REPLACE INTO target_table ...
      params: ${transform.output}

  # Update checkpoint
  - name: update-checkpoint
    condition: ${load.success}
    operation: storage
    state:
      set:
        last_sync_id: ${extract.output[extract.output.length - 1].id}
        last_sync_time: ${new Date().toISOString()}
    config:
      type: kv
      action: put
      key: sync_checkpoint
      value:
        last_sync_id: ${extract.output[extract.output.length - 1].id}
        last_sync_time: ${new Date().toISOString()}

Real-time Streaming ETL

ensemble: streaming-etl

agents:
  # Receive event
  - name: validate-event
    operation: validate
    inputs:
      data: ${input.event}
      schema: ${[email protected]}

  # Enrich with context
  - name: enrich
    operation: storage
    config:
      type: kv
      action: get
      key: user-${input.event.user_id}

  # Transform
  - name: transform
    operation: code
    inputs:
      data:
        event: ${validate-event.output.data}
        user: ${enrich.output}
      template:
        event_id: ${data.event.id}
        event_type: ${data.event.type}
        user_id: ${data.event.user_id}
        user_segment: ${data.user.segment}
        properties: ${data.event.properties}
        timestamp: ${Date.now()}

  # Write to multiple destinations (parallel)
  - name: write-analytics-db
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: INSERT INTO events ...
      params: ${transform.output}

  - name: write-data-lake
    operation: storage
    config:
      type: r2
      action: put
      key: events/${new Date().toISOString().split('T')[0]}/${transform.output.event_id}.json
      value: ${JSON.stringify(transform.output)}

  - name: send-to-warehouse
    operation: http
    config:
      url: https://warehouse.example.com/api/ingest
      body: ${transform.output}

Best Practices

1. Batch Processing

# Process in chunks
agents:
  - name: extract
    operation: storage
    config:
      query: SELECT * FROM source LIMIT 1000 OFFSET ${input.offset}

2. Error Handling

# Always handle failures
agents:
  - name: transform
    operation: code

  - name: log-error
    condition: ${transform.failed}
    operation: data
    config:
      backend: d1
      binding: DB
      operation: execute
      sql: INSERT INTO etl_errors ...

3. Idempotency

# Use INSERT OR REPLACE
config:
  query: INSERT OR REPLACE INTO target ...

4. Checkpointing

# Track progress
state:
  schema:
    last_processed_id: number

5. Monitoring

# Log metrics
agents:
  - name: log-metrics
    operation: storage
    config:
      query: |
        INSERT INTO etl_metrics (
          pipeline, records_processed, duration, timestamp
        ) VALUES (?, ?, ?, ?)

Next Steps

Code Operation

Data transformation

Validate Operation

Data validation

storage Operation

Database access

Event-Driven Workflow

Real-time processing

Conductor

Getting Started

Core Concepts

Building

Components

Operations Reference

Plugins

Starter Kit

Playbooks

Reference

Basic ETL Pattern

Database to Database

API to Data Warehouse

CSV Import with Validation

Incremental Sync

Real-time Streaming ETL

Best Practices

Next Steps

Code Operation

Validate Operation

storage Operation

Event-Driven Workflow

Conductor

Getting Started

Core Concepts

Building

Components

Operations Reference

Plugins

Starter Kit

Playbooks

Reference

​Basic ETL Pattern

​Database to Database

​API to Data Warehouse

​CSV Import with Validation

​Incremental Sync

​Real-time Streaming ETL

​Best Practices

​Next Steps

Code Operation

Validate Operation

storage Operation

Event-Driven Workflow

Basic ETL Pattern

Database to Database

API to Data Warehouse

CSV Import with Validation

Incremental Sync

Real-time Streaming ETL

Best Practices

Next Steps