Data Processing Pipelines Technical Specification

Overview

Data processing pipelines form the foundation of AI engineering workflows, handling data ingestion, cleaning, transformation, enrichment, and quality assurance. These pipelines leverage both Claude and Gemini for intelligent data manipulation.

Pipeline Categories

1. Data Cleaning Pipelines

1.1 Standard Data Cleaning Pipeline

ID: data-cleaning-standard
Purpose: Multi-stage data cleaning with validation
Complexity: Medium

Workflow Steps:

Data Profiling (Gemini)
- Analyze data structure and types
- Identify anomalies and patterns
- Generate cleaning recommendations
Schema Validation (Gemini Function)
- Validate against expected schema
- Report schema violations
- Suggest schema corrections
Data Cleansing (Claude)
- Remove duplicates
- Handle missing values
- Standardize formats
- Fix encoding issues
Quality Check (Gemini)
- Verify cleaning effectiveness
- Generate quality report
- Flag remaining issues

Configuration Example:

workflow:
  name: "data_cleaning_standard"
  description: "Comprehensive data cleaning with quality assurance"
  
  defaults:
    workspace_dir: "./workspace/data_cleaning"
    output_dir: "./outputs/cleaned_data"
    
  steps:
    - name: "profile_data"
      type: "gemini"
      role: "data_analyst"
      prompt_parts:
        - type: "static"
          content: "Analyze this dataset and identify data quality issues:"
        - type: "file"
          path: "{input_file}"
      options:
        model: "gemini-2.5-flash"
        temperature: 0.3
        
    - name: "validate_schema"
      type: "gemini"
      role: "schema_validator"
      gemini_functions:
        - name: "validate_schema"
          parameters:
            schema_file: "{schema_path}"
            data_file: "{input_file}"
            
    - name: "clean_data"
      type: "claude"
      role: "data_engineer"
      prompt_parts:
        - type: "static"
          content: "Clean the data based on these issues:"
        - type: "previous_response"
          step: "profile_data"
      options:
        tools: ["write", "edit", "read"]
        output_format: "json"
        
    - name: "quality_check"
      type: "gemini"
      role: "quality_assurance"
      prompt_parts:
        - type: "static"
          content: "Verify the cleaned data quality"
        - type: "previous_response"
          step: "clean_data"
          field: "cleaned_file_path"

1.2 Advanced Data Cleaning Pipeline

ID: data-cleaning-advanced
Purpose: ML-powered cleaning with anomaly detection
Complexity: High

Additional Features:

Outlier detection using statistical methods
Pattern-based cleaning rules
Machine learning for missing value imputation
Automated data type inference

2. Data Enrichment Pipelines

2.1 Entity Extraction Pipeline

ID: data-enrichment-entity
Purpose: Extract and enrich entities from unstructured data
Complexity: High

Workflow Steps:

Text Preprocessing (Claude)
- Normalize text format
- Handle encoding issues
- Segment into processable chunks
Entity Recognition (Parallel Claude)
- Identify persons, organizations, locations
- Extract dates, amounts, identifiers
- Detect custom entity types
Entity Enrichment (Gemini Functions)
- Lookup additional information
- Validate entity relationships
- Cross-reference with knowledge bases
Data Integration (Claude)
- Merge enriched data
- Resolve conflicts
- Generate enriched dataset

Key Components:

# Reusable entity extraction prompt
components/prompts/entity_extraction.yaml:
  variables:
    - text_content
    - entity_types
    - extraction_rules
    
  template: |
    Extract the following entity types from the text:
    Entity Types: {entity_types}
    
    Rules:
    {extraction_rules}
    
    Text:
    {text_content}
    
    Return as structured JSON with confidence scores.

2.2 Data Augmentation Pipeline

ID: data-enrichment-augmentation
Purpose: Intelligently augment datasets for ML training
Complexity: Medium

Features:

Synthetic data generation
Data balancing techniques
Feature engineering
Cross-validation setup

3. Data Transformation Pipelines

3.1 Format Conversion Pipeline

ID: data-transformation-format
Purpose: Convert between data formats intelligently
Complexity: Low

Supported Conversions:

CSV ↔ JSON ↔ XML ↔ Parquet
Schema-aware transformations
Nested structure handling
Batch processing support

Workflow Example:

steps:
  - name: "analyze_source"
    type: "gemini"
    role: "format_analyzer"
    prompt: "Analyze the structure of this {source_format} file"
    
  - name: "generate_mapping"
    type: "claude"
    role: "mapping_generator"
    prompt: "Create transformation rules from {source_format} to {target_format}"
    
  - name: "transform_data"
    type: "claude_batch"
    role: "data_transformer"
    batch_size: 1000
    prompt: "Apply transformation rules to convert data"

3.2 Data Normalization Pipeline

ID: data-transformation-normalize
Purpose: Standardize data across sources
Complexity: Medium

Normalization Types:

Value normalization (scaling, encoding)
Schema normalization
Format standardization
Reference data alignment

4. Data Quality Pipelines

4.1 Comprehensive Quality Assessment

ID: data-quality-comprehensive
Purpose: Full data quality evaluation and reporting
Complexity: High

Quality Dimensions:

Completeness: Missing value analysis
Accuracy: Validation against rules
Consistency: Cross-field validation
Timeliness: Data freshness checks
Uniqueness: Duplicate detection
Validity: Format and range checks

Implementation Pattern:

steps:
  - name: "parallel_quality_checks"
    type: "parallel_claude"
    instances:
      - role: "completeness_checker"
        prompt: "Analyze data completeness"
      - role: "accuracy_validator"
        prompt: "Check data accuracy"
      - role: "consistency_analyzer"
        prompt: "Validate data consistency"
        
  - name: "generate_report"
    type: "claude_smart"
    preset: "analysis"
    prompt: "Generate comprehensive quality report"
    output_file: "quality_report.md"

4.2 Real-time Quality Monitoring

ID: data-quality-monitoring
Purpose: Continuous quality monitoring with alerts
Complexity: Medium

Features:

Streaming data quality checks
Anomaly detection
Alert generation
Quality trend analysis

Reusable Components

Validation Components

# components/steps/validation/schema_validator.yaml
component:
  id: "schema-validator"
  type: "step"
  
  implementation:
    type: "gemini"
    functions:
      - name: "validate_against_schema"
        description: "Validate data against JSON Schema"
        parameters:
          data:
            type: "object"
          schema:
            type: "object"
          strict_mode:
            type: "boolean"
            default: true

Transformation Components

# components/transformers/data/normalizer.yaml
component:
  id: "data-normalizer"
  type: "transformer"
  
  strategies:
    - min_max_scaling
    - z_score_normalization
    - decimal_scaling
    - log_transformation

Quality Check Components

# components/steps/quality/duplicate_detector.yaml
component:
  id: "duplicate-detector"
  type: "step"
  
  algorithms:
    - exact_match
    - fuzzy_match
    - semantic_similarity
  
  configuration:
    threshold: 0.95
    columns: ["all"]

Performance Considerations

1. Batch Processing

Use claude_batch for large datasets
Implement chunking strategies
Configure appropriate batch sizes

2. Parallel Execution

Leverage parallel_claude for independent tasks
Distribute workload effectively
Manage memory consumption

3. Caching Strategies

Cache intermediate results
Implement smart checkpointing
Use workspace effectively

Error Handling

1. Data Validation Errors

error_handlers:
  schema_validation_error:
    action: "log_and_continue"
    fallback: "use_previous_schema"
    
  missing_required_field:
    action: "attempt_recovery"
    strategy: "infer_from_context"

2. Processing Failures

Implement retry mechanisms
Partial result preservation
Graceful degradation

Testing Strategies

1. Unit Tests

Test individual transformation functions
Validate cleaning algorithms
Check enrichment accuracy

2. Integration Tests

End-to-end pipeline execution
Data quality benchmarks
Performance testing

3. Sample Data Sets

test_data:
  small_dataset:
    size: "1MB"
    records: 1000
    issues: ["duplicates", "missing_values"]
    
  large_dataset:
    size: "100MB"
    records: 100000
    issues: ["encoding", "schema_drift"]

Monitoring and Metrics

1. Pipeline Metrics

Execution time per stage
Data volume processed
Quality improvement scores
Resource utilization

2. Quality Metrics

Data quality score trends
Issue detection rates
Cleaning effectiveness
Enrichment coverage

Best Practices

Start Simple: Begin with standard cleaning pipeline
Profile First: Always analyze data before processing
Validate Often: Check data quality at each stage
Document Changes: Track all transformations
Test Thoroughly: Use representative test data
Monitor Continuously: Track quality metrics

Future Enhancements

Real-time Processing: Stream processing support
ML Integration: Advanced anomaly detection
Custom Rules Engine: User-defined cleaning rules
Visual Pipeline Builder: GUI for pipeline creation
Auto-optimization: Performance tuning based on data characteristics

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