Data Engineering Agent

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Overview

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Demo: Engineering Agent in Action

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Key Capabilities

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🔧 Current dbt Workflow

• Data Profiling: Analyzes raw data sources to understand structure and content
• Schema Detection: Automatically identifies data types and relationships
• Transformation Generation: Creates SQL transformations based on requirements
• dbt Model Creation: Generates properly structured dbt models with configurations
• Testing & Validation: Runs data quality tests and validates model outputs
• GitHub Integration: Creates pull requests for human review before deployment

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Integration Capabilities

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With Other Agents

• ← Data Retrieval Agent: Receives raw data from various sources
• → Data Analysis Agent: Provides clean, transformed data for analysis
• → Data Visualization Agent: Supplies optimized datasets for dashboards
• ↔ Governance Agent: Collaborates on data lineage and quality metrics

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Tool Integrations

• Cloud Platforms: AWS, GCP, Azure data services
• Data Warehouses: Snowflake, BigQuery, Redshift, Databricks
• Version Control: Git integration for dbt project management

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🏗️ Model Generation

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What We Currently Support

• Basic dbt Models: Creates SQL transformations wrapped in dbt model structure
• Model Configuration: Adds appropriate materialization and configuration settings
• Data Quality Tests: Generates basic tests for data validation

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Transformation Capabilities

-- Example: Automated customer segmentation model
{{ config(materialized='table') }}

WITH customer_metrics AS (
    SELECT 
        customer_id,
        SUM(order_amount) AS total_spent,
        COUNT(order_id) AS order_count,
        AVG(order_amount) AS avg_order_value,
        DATEDIFF(CURRENT_DATE, MAX(order_date)) AS days_since_last_order
    FROM {{ ref('fact_orders') }}
    GROUP BY customer_id
),
customer_segments AS (
    SELECT *,
        CASE 
            WHEN total_spent > 10000 AND days_since_last_order <= 30 THEN 'VIP_Active'
            WHEN total_spent > 5000 AND days_since_last_order <= 60 THEN 'High_Value'
            WHEN days_since_last_order <= 90 THEN 'Active'
            WHEN days_since_last_order <= 365 THEN 'At_Risk'
            ELSE 'Churned'
        END AS segment
    FROM customer_metrics
)
SELECT * FROM customer_segments

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Current Workflow

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Engineering Agent Process Flow

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How It Works

1. Data Profiling: Agent analyzes your raw data to understand structure, quality, and patterns
2. Schema Detection: Automatically identifies data types, relationships, and constraints
3. Transformation Generation: Creates the SQL logic needed for your specific requirements
4. dbt Model Creation: Wraps transformations in proper dbt model structure with configurations
5. Testing & Validation: Runs generated tests to ensure data quality and model correctness
6. GitHub PR Creation: Creates a pull request with all generated code for review
7. Human Review: Requires human approval before any changes are deployed
8. Deployment: Once approved, models are deployed to your data warehouse

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Future DBT Integrations Roadmap

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Automated Model Generation

# Generated dbt_project.yml configuration
name: 'brightagent_analytics'
version: '1.0.0'
config-version: 2

model-paths: ["models"]
analysis-paths: ["analysis"]
test-paths: ["tests"]
seed-paths: ["data"]
macro-paths: ["macros"]
snapshot-paths: ["snapshots"]

target-path: "target"
clean-targets:
  - "target"
  - "dbt_packages"

models:
  brightagent_analytics:
    staging:
      +materialized: view
      +schema: staging
    intermediate:
      +materialized: ephemeral
    marts:
      +materialized: table
      +schema: marts

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Intelligent Testing

-- Automatically generated data tests
{{ config(severity='error') }}

-- Test for referential integrity
SELECT order_id
FROM {{ ref('fact_orders') }}
WHERE customer_id NOT IN (
    SELECT customer_id 
    FROM {{ ref('dim_customers') }}
)

-- Test for data freshness
SELECT MAX(updated_at) AS last_update
FROM {{ ref('fact_orders') }}
WHERE DATE(updated_at) < CURRENT_DATE - INTERVAL 1 DAY

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Data Quality Management

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Automated Quality Checks

• Completeness: Missing value detection and handling
• Consistency: Cross-table validation and referential integrity
• Accuracy: Business rule validation and anomaly detection
• Timeliness: Data freshness monitoring and SLA tracking

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Quality Rules Engine

quality_rules:
  fact_orders:
    - test: not_null
      columns: [order_id, customer_id, order_date]
    - test: unique
      columns: [order_id]
    - test: relationships
      to: ref('dim_customers')
      field: customer_id
    - test: accepted_values
      column: order_status
      values: ['pending', 'confirmed', 'shipped', 'delivered', 'cancelled']
    - test: custom_business_rule
      sql: "order_amount > 0 AND order_amount < 1000000"

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Performance Optimization

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Query Optimization Strategies

• Automatic Indexing: Intelligent index recommendations based on query patterns
• Partitioning Logic: Automatic table partitioning for large datasets
• Materialization Strategy: Optimal materialization choices (view vs table vs incremental)
• Resource Management: Dynamic resource allocation based on workload

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Incremental Processing

-- Automatically generated incremental model
{{ config(
    materialized='incremental',
    unique_key='order_id',
    on_schema_change='fail'
) }}

SELECT 
    order_id,
    customer_id,
    order_date,
    order_amount,
    created_at,
    updated_at
FROM {{ source('raw_data', 'orders') }}

{% if is_incremental() %}
    WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}

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Configuration Management

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Pipeline Configuration

pipeline_settings:
  default_batch_size: 100000
  max_memory_usage: "8GB"
  parallel_execution: true
  retry_policy:
    max_attempts: 3
    backoff_strategy: "exponential"
  
data_freshness:
  critical_tables:
    - name: "fact_orders"
      max_age_hours: 2
    - name: "dim_customers" 
      max_age_hours: 24
      
quality_thresholds:
  completeness_min: 0.95
  accuracy_min: 0.98
  timeliness_max_delay: "1 hour"

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Environment Management

environments:
  development:
    target: dev
    schema_suffix: "_dev"
    full_refresh: true
    
  staging:
    target: staging
    schema_suffix: "_staging"
    run_tests: true
    
  production:
    target: prod
    schema_suffix: ""
    require_approval: true
    backup_before_deploy: true

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Monitoring & Observability

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Pipeline Metrics

• Execution Time: Pipeline and model-level performance tracking
• Data Volume: Record counts and data size monitoring
• Success Rates: Pipeline success/failure rates and error patterns
• Resource Usage: CPU, memory, and storage utilization

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Alerting System

alerts:
  pipeline_failure:
    condition: "execution_status = 'failed'"
    severity: "critical"
    notification: ["slack", "email"]
    
  data_quality_breach:
    condition: "quality_score < 0.95"
    severity: "warning"
    auto_remediation: true
    
  performance_degradation:
    condition: "execution_time > baseline * 1.5"
    severity: "warning"
    investigation_required: true

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Best Practices Implementation

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Code Quality Standards

• Modular Design: Reusable macros and standardized patterns
• Documentation: Automatic documentation generation for all models
• Testing: Comprehensive test coverage for data quality
• Version Control: Proper branching and deployment strategies

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Security & Governance

• Access Control: Role-based permissions for pipeline components
• Data Masking: Automatic PII detection and masking
• Audit Logging: Comprehensive logging of all transformation activities
• Compliance: Automated compliance checking for regulations (GDPR, CCPA)

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Troubleshooting & Support

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Common Issues & Solutions

1. Pipeline Failures: Automatic retry with exponential backoff
2. Performance Issues: Query optimization and resource scaling
3. Data Quality Problems: Automated data profiling and anomaly detection
4. Schema Changes: Intelligent schema evolution handling

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Diagnostic Tools

• Pipeline performance analyzer
• Data lineage visualization
• Query execution plan optimizer
• Resource utilization monitor
• Data quality dashboard

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Deployment & CI/CD

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Automated Deployment Pipeline

deployment_pipeline:
  stages:
    - lint_and_validate
    - run_tests
    - staging_deployment
    - integration_tests
    - production_deployment
    - post_deployment_validation
    
  approval_gates:
    - stage: staging_deployment
      required_approvers: 1
    - stage: production_deployment
      required_approvers: 2
      additional_checks: ["security_scan", "performance_test"]