# 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**:
1. **Data Profiling** (Gemini)
- Analyze data structure and types
- Identify anomalies and patterns
- Generate cleaning recommendations
2. **Schema Validation** (Gemini Function)
- Validate against expected schema
- Report schema violations
- Suggest schema corrections
3. **Data Cleansing** (Claude)
- Remove duplicates
- Handle missing values
- Standardize formats
- Fix encoding issues
4. **Quality Check** (Gemini)
- Verify cleaning effectiveness
- Generate quality report
- Flag remaining issues
**Configuration Example**:
```yaml
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**:
1. **Text Preprocessing** (Claude)
- Normalize text format
- Handle encoding issues
- Segment into processable chunks
2. **Entity Recognition** (Parallel Claude)
- Identify persons, organizations, locations
- Extract dates, amounts, identifiers
- Detect custom entity types
3. **Entity Enrichment** (Gemini Functions)
- Lookup additional information
- Validate entity relationships
- Cross-reference with knowledge bases
4. **Data Integration** (Claude)
- Merge enriched data
- Resolve conflicts
- Generate enriched dataset
**Key Components**:
```yaml
# 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**:
```yaml
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**:
1. **Completeness**: Missing value analysis
2. **Accuracy**: Validation against rules
3. **Consistency**: Cross-field validation
4. **Timeliness**: Data freshness checks
5. **Uniqueness**: Duplicate detection
6. **Validity**: Format and range checks
**Implementation Pattern**:
```yaml
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
```yaml
# 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
```yaml
# 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
```yaml
# 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
```yaml
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
```yaml
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
1. **Start Simple**: Begin with standard cleaning pipeline
2. **Profile First**: Always analyze data before processing
3. **Validate Often**: Check data quality at each stage
4. **Document Changes**: Track all transformations
5. **Test Thoroughly**: Use representative test data
6. **Monitor Continuously**: Track quality metrics
## Future Enhancements
1. **Real-time Processing**: Stream processing support
2. **ML Integration**: Advanced anomaly detection
3. **Custom Rules Engine**: User-defined cleaning rules
4. **Visual Pipeline Builder**: GUI for pipeline creation
5. **Auto-optimization**: Performance tuning based on data characteristics
