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  • Overview
    • Getting Started
    • Platform Overview
  • Features
    • Features Overview
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    • Universal Integrations
    • Communication Channels
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  • Use Cases Overview
  • Financial Services
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  • Smart ETL
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  • Reference
    • API Reference
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  • Endpoints
    • Workflows API
    • Tools API
    • KDL (Data Lake) API
    • OpenAI-Compatible API
    • A2A Protocol
    • Skills API
    • Knowledge Base (RAG) API
    • Communication Channels

Smart ETL

Intelligent Data Integration & Transformation

Transform your data pipelines with AI-powered ETL that automatically discovers schemas, suggests transformations, handles errors intelligently, and maintains complete data lineage. Move from brittle, manual pipelines to self-healing, adaptive data integration.

Traditional ETL Challenges

Organizations struggle with conventional data integration:

Common Problems:

  • Weeks to build new data pipelines
  • Pipelines break when sources change
  • Manual error investigation and resolution
  • No visibility into data transformations
  • Inconsistent data quality

The Kaman Smart ETL Solution

AI-Powered Pipeline Creation

Build pipelines in minutes, not weeks:

Smart Features:

FeatureCapability
Auto-DiscoveryAutomatic schema detection and profiling
Smart MappingAI-suggested field mappings
Type InferenceAutomatic data type detection
Transformation SuggestionsRecommended cleansing and formatting
Quality RulesAuto-generated validation rules

Intelligent Schema Management

Handle schema changes automatically:

Schema Intelligence:

  • Real-time schema monitoring
  • Automatic drift detection
  • Impact analysis on downstream systems
  • Suggested adaptations
  • Version control for schemas

Self-Healing Pipelines

Recover from errors automatically:

Self-Healing Capabilities:

Error TypeAuto-Resolution
Connection FailuresAutomatic retry with backoff
Data Type MismatchesSmart type coercion
Missing ValuesDefault value application
Format VariationsPattern-based normalization
Duplicate RecordsIntelligent deduplication

Key Capabilities

Visual Pipeline Builder

Design pipelines without code:

Builder Features:

  • Drag-and-drop interface
  • Pre-built transformation library
  • Real-time preview
  • Inline data quality checks
  • Version control

Transformation Library

Rich set of built-in transformations:

CategoryTransformations
Data CleansingTrim, case conversion, null handling, deduplication
Type ConversionDate parsing, number formatting, encoding
StructuralFlatten, pivot, unpivot, split, merge
AggregationSum, average, count, min/max, custom
EnrichmentLookup, geocoding, validation, derived fields
AdvancedMachine learning, pattern matching, fuzzy matching

Incremental Processing

Process only what's changed:

Incremental Modes:

  • Change Data Capture (CDC)
  • Timestamp-based incremental
  • Watermark processing
  • Full refresh with merge
  • Append-only loading

Data Quality Integration

Built-In Quality Checks

Validate data as it flows:

Quality Rule Types:

  • Completeness checks
  • Format validation
  • Range/boundary checks
  • Referential integrity
  • Business rule validation
  • Statistical anomaly detection

Quality Metrics & Monitoring

Track pipeline health continuously:

MetricDescription
ThroughputRecords processed per time unit
LatencyEnd-to-end processing time
Error RatePercentage of failed records
Quality ScoreComposite data quality rating
SLA ComplianceMeeting delivery commitments

Complete Data Lineage

End-to-End Tracking

Know where every data point comes from:

Lineage Capabilities:

  • Column-level lineage
  • Transformation documentation
  • Impact analysis
  • Root cause tracing
  • Compliance documentation

Lineage Visualization

Interactive lineage exploration:

  • Forward impact analysis
  • Backward dependency tracking
  • Transformation drill-down
  • Time-based lineage history
  • Export for documentation

Intelligent Memory Integration

Pattern Learning

Kaman learns from your data:

  • Common transformation patterns
  • Typical error resolutions
  • Optimal processing configurations
  • Quality rule suggestions
  • Performance optimizations

Proactive Recommendations

AI suggests improvements:

Recommendation Types:

  • Partition strategies
  • Caching opportunities
  • Parallel processing options
  • Index suggestions
  • Resource optimization

Orchestration & Scheduling

Flexible Scheduling

Run pipelines when needed:

Trigger TypeUse Case
ScheduledRegular batch processing
Event-DrivenReal-time data arrival
API-TriggeredOn-demand processing
Dependency-BasedAfter upstream completion
Data-DrivenWhen data threshold met

Pipeline Dependencies

Manage complex workflows:

Benefits

Development Efficiency

BenefitImpact
Pipeline Creation80% faster with AI assistance
Maintenance70% reduction in manual fixes
Schema ChangesAutomatic adaptation
TestingBuilt-in validation

Operational Excellence

BenefitImpact
UptimeSelf-healing reduces failures
Data QualityContinuous validation
VisibilityComplete lineage and monitoring
ComplianceAutomated documentation

Business Value

BenefitImpact
Time to ValueDays instead of weeks
TrustVerified data quality
AgilityRapid adaptation to changes
CostReduced development and operations

Implementation Approach

Phase 1: Connect

  1. Source Integration

    • Inventory data sources
    • Establish connections
    • Enable schema discovery

  2. Target Setup

    • Configure data lake/warehouse
    • Define target schemas
    • Set up access controls

Phase 2: Build

  1. Pipeline Development

    • Use AI suggestions for mappings
    • Configure transformations
    • Set up quality rules

  2. Testing & Validation

    • Preview transformations
    • Validate output quality
    • Performance testing

Phase 3: Operate

  1. Deployment

    • Schedule pipelines
    • Configure monitoring
    • Set up alerting

  2. Optimization

    • Review AI recommendations
    • Tune performance
    • Expand coverage

Getting Started

Assessment Questions

  1. What data sources need integration?
  2. What is your current pipeline complexity?
  3. How often do source schemas change?
  4. What are your data quality requirements?
  5. What is your target latency?

Quick Wins

Start with high-value pipelines:

  • Critical business data integration
  • Frequently failing pipelines
  • Manual data processing tasks
  • High-volume data sources

Building the Platform

Expand systematically:

  • Add data sources incrementally
  • Migrate existing pipelines
  • Enable advanced transformations
  • Implement real-time processing

Smart ETL - Intelligent, self-healing data integration

On this page

  • Intelligent Data Integration & Transformation
  • Traditional ETL Challenges
  • The Kaman Smart ETL Solution
  • AI-Powered Pipeline Creation
  • Intelligent Schema Management
  • Self-Healing Pipelines
  • Key Capabilities
  • Visual Pipeline Builder
  • Transformation Library
  • Incremental Processing
  • Data Quality Integration
  • Built-In Quality Checks
  • Quality Metrics & Monitoring
  • Complete Data Lineage
  • End-to-End Tracking
  • Lineage Visualization
  • Intelligent Memory Integration
  • Pattern Learning
  • Proactive Recommendations
  • Orchestration & Scheduling
  • Flexible Scheduling
  • Pipeline Dependencies
  • Benefits
  • Development Efficiency
  • Operational Excellence
  • Business Value
  • Implementation Approach
  • Phase 1: Connect
  • Phase 2: Build
  • Phase 3: Operate
  • Getting Started
  • Assessment Questions
  • Quick Wins
  • Building the Platform