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Overview

The Data Retrieval Agent is responsible for accessing, extracting, and preparing data from various sources and formats. It serves as the primary interface between raw data sources and the BrightAgent ecosystem, enabling seamless data access for analysis, transformation, and visualization.

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Demo: Retrieval Agent in Action

Watch the Data Retrieval Agent demonstrate its capabilities in this end-to-end workflow: This demo starts at 10:00 and shows the retrieval agent highlighting data source connection, extraction, and preparation processes. Watch for approximately 4 minutes to see the complete retrieval workflow.

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Key Capabilities

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🔗 Multi-Source Connectivity

  • Database Connections: PostgreSQL, MySQL, SQL Server, Oracle, BigQuery, Snowflake, Redshift
  • File Formats: CSV, JSON, Parquet, Excel, XML, Avro, ORC
  • APIs & Web Services: REST APIs, GraphQL endpoints, SOAP services
  • Cloud Storage: AWS S3, Google Cloud Storage, Azure Blob Storage
  • Streaming Data: Kafka, Kinesis, Pub/Sub

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📥 Data Ingestion Workflows

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1. Source Discovery

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2. Data Extraction Process

  • Incremental Loading: Fetch only new or modified records
  • Full Refresh: Complete data extraction for initial loads
  • Real-time Streaming: Continuous data ingestion from live sources
  • Scheduled Pulls: Automated data retrieval at specified intervals

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🧹 Data Preparation

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Automatic Data Cleaning

  • Null Value Handling: Identification and treatment of missing data
  • Data Type Inference: Automatic detection and conversion of data types
  • Duplicate Detection: Identification and flagging of duplicate records
  • Format Standardization: Consistent formatting across different sources

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Data Quality Checks

  • Completeness Validation: Ensuring all required fields are present
  • Accuracy Verification: Cross-referencing with known good sources
  • Consistency Checks: Validating data against business rules
  • Freshness Monitoring: Tracking data recency and update frequency

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Workflow Examples

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Example 1: Database Retrieval

-- The agent can execute complex queries across multiple sources
SELECT 
    u.user_id,
    u.email,
    o.order_total,
    p.product_name
FROM users u
LEFT JOIN orders o ON u.user_id = o.user_id
LEFT JOIN products p ON o.product_id = p.product_id
WHERE o.created_date >= '2024-01-01'

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Integration Points

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With Other Agents

  • → Data Engineering Agent: Provides raw data for transformation pipelines
  • → Data Analysis Agent: Supplies clean data for analytical workloads
  • → Data Visualization Agent: Feeds structured data for dashboard creation
  • → Data Governance Agent: Reports on data lineage and quality metrics

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Session Management

  • In-Memory Processing: Loads frequently accessed data into session memory
  • Caching Strategy: Intelligent caching of commonly requested datasets
  • Resource Optimization: Manages memory usage and query performance

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Configuration Options

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Connection Settings

data_sources:
  - name: "production_db"
    type: "postgresql"
    host: "prod-db.company.com"
    database: "analytics"
    schema: "public"
    connection_pool_size: 10
    
  - name: "customer_api"
    type: "rest_api"
    base_url: "https://api.customer-system.com"
    authentication: "oauth2"
    rate_limit: 100  # requests per minute

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Monitoring & Alerts

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Performance Metrics

  • Query Execution Time: Average and percentile response times
  • Data Volume: Records processed per hour/day
  • Error Rates: Failed retrieval attempts and retry success rates
  • Source Availability: Uptime monitoring for connected data sources

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Alert Conditions

  • Source connectivity issues
  • Data quality threshold violations
  • Performance degradation beyond acceptable limits
  • Unexpected data schema changes

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Best Practices

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Security

  • Credential Management: Secure storage and rotation of database credentials
  • Data Encryption: End-to-end encryption for data in transit and at rest
  • Access Control: Role-based permissions for data source access
  • Audit Logging: Comprehensive logging of all data access activities

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Performance Optimization

  • Query Optimization: Intelligent query planning and execution
  • Parallel Processing: Concurrent data retrieval from multiple sources
  • Compression: Data compression during transfer and storage
  • Indexing Strategy: Optimal indexing for frequently accessed data patterns

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Troubleshooting

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Common Issues

  1. Connection Timeouts: Check network connectivity and increase timeout values
  2. Memory Overflow: Reduce batch sizes or implement streaming for large datasets
  3. Authentication Failures: Verify credentials and refresh tokens as needed
  4. Schema Mismatches: Update data type mappings and field configurations

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Diagnostic Tools

  • Connection test utilities
  • Query performance analyzer
  • Data quality assessment reports
  • Source health monitoring dashboard