Data Processing Pipeline¶

OpenML Crawler provides a comprehensive data processing pipeline that transforms raw data from various sources into clean, structured, and analysis-ready datasets. The pipeline handles data validation, cleaning, transformation, enrichment, and quality assurance.

Pipeline Architecture¶

The data processing pipeline consists of several interconnected stages that work together to ensure data quality and consistency:

graph TD
    A[Data Ingestion] --> B[Data Validation]
    B --> C[Data Cleaning]
    C --> D[Data Transformation]
    D --> E[Data Enrichment]
    E --> F[Data Quality Assurance]
    F --> G[Data Storage]
    G --> H[Data Export]

Core Components¶

Data Ingestion: Collects data from various sources and formats
Data Validation: Ensures data meets quality and consistency standards
Data Cleaning: Removes errors, duplicates, and inconsistencies
Data Transformation: Converts data into desired formats and structures
Data Enrichment: Adds additional context and derived features
Data Quality Assurance: Validates processed data against quality metrics
Data Storage: Persists processed data in appropriate formats
Data Export: Makes data available for analysis and consumption

Data Ingestion¶

Source Connectors¶

The pipeline integrates with all OpenML Crawler connectors to ingest data from diverse sources:

from openmlcrawler.processing import DataIngestionPipeline

pipeline = DataIngestionPipeline()

# Ingest data from multiple sources
sources = [
    {"type": "weather", "source": "openweather", "location": "New York"},
    {"type": "finance", "source": "alphavantage", "symbol": "AAPL"},
    {"type": "social", "source": "twitter", "query": "#machinelearning"},
    {"type": "news", "source": "newsapi", "category": "technology"}
]

ingested_data = pipeline.ingest_from_sources(
    sources=sources,
    date_range=("2023-01-01", "2023-12-31"),
    batch_size=1000
)

File Format Support¶

The pipeline supports various input formats:

Structured Data: CSV, JSON, XML, Parquet, Avro
Semi-structured Data: HTML, XML, JSON Lines
Unstructured Data: Text files, logs, documents
Binary Data: Images, audio, video (metadata extraction)
Database Data: SQL query results, NoSQL documents

from openmlcrawler.processing import FileProcessor

processor = FileProcessor()

# Process different file formats
csv_data = processor.process_csv("data/sales.csv", delimiter=",")
json_data = processor.process_json("data/api_response.json")
xml_data = processor.process_xml("data/config.xml")
parquet_data = processor.process_parquet("data/analytics.parquet")

Streaming Data¶

For real-time data processing:

from openmlcrawler.processing import StreamingProcessor

processor = StreamingProcessor()

# Process streaming data
@processor.stream_handler
def process_stream_data(data_batch):
    # Process each batch of streaming data
    cleaned_data = processor.clean_batch(data_batch)
    transformed_data = processor.transform_batch(cleaned_data)
    return transformed_data

# Start streaming pipeline
processor.start_streaming(
    sources=["kafka_topic", "websocket_feed"],
    batch_size=100,
    window_seconds=60
)

Data Validation¶

Schema Validation¶

Validate data against predefined schemas:

from openmlcrawler.processing import SchemaValidator

validator = SchemaValidator()

# Define data schema
schema = {
    "type": "object",
    "properties": {
        "id": {"type": "integer"},
        "name": {"type": "string", "minLength": 1},
        "price": {"type": "number", "minimum": 0},
        "category": {"type": "string", "enum": ["A", "B", "C"]},
        "timestamp": {"type": "string", "format": "date-time"}
    },
    "required": ["id", "name", "price"]
}

# Validate data against schema
validation_result = validator.validate_data(
    data=input_data,
    schema=schema,
    strict_mode=True
)

# Get validation report
report = validator.generate_validation_report(validation_result)

Data Type Validation¶

Ensure data types match expected formats:

from openmlcrawler.processing import TypeValidator

type_validator = TypeValidator()

# Define type mappings
type_mappings = {
    "id": "int64",
    "name": "string",
    "price": "float64",
    "date": "datetime64[ns]",
    "active": "boolean"
}

# Validate and convert data types
validated_data = type_validator.validate_types(
    data=input_data,
    type_mappings=type_mappings,
    convert_types=True,
    handle_errors="coerce"  # or "raise" or "ignore"
)

Business Rule Validation¶

Apply domain-specific business rules:

from openmlcrawler.processing import BusinessRuleValidator

rule_validator = BusinessRuleValidator()

# Define business rules
rules = [
    {
        "name": "price_range",
        "condition": "price > 0 and price < 10000",
        "action": "flag",
        "severity": "error"
    },
    {
        "name": "date_future",
        "condition": "timestamp <= current_date",
        "action": "correct",
        "correction": "set_to_current_date"
    },
    {
        "name": "category_valid",
        "condition": "category in ['A', 'B', 'C', 'D']",
        "action": "reject",
        "severity": "warning"
    }
]

# Apply business rules
validated_data = rule_validator.apply_rules(
    data=input_data,
    rules=rules,
    fail_fast=False  # Continue processing even if some rules fail
)

Data Cleaning¶

Missing Value Handling¶

Handle missing values with various strategies:

from openmlcrawler.processing import MissingValueHandler

handler = MissingValueHandler()

# Different strategies for missing values
strategies = {
    "numeric_column": "mean",  # mean, median, mode, constant
    "categorical_column": "mode",  # mode, constant, drop
    "date_column": "interpolate",  # interpolate, forward_fill, backward_fill
    "text_column": "empty_string"  # empty_string, drop
}

# Handle missing values
cleaned_data = handler.handle_missing_values(
    data=input_data,
    strategies=strategies,
    threshold=0.5  # Drop columns with >50% missing values
)

Duplicate Removal¶

Remove duplicate records:

from openmlcrawler.processing import DuplicateRemover

remover = DuplicateRemover()

# Remove exact duplicates
deduplicated_data = remover.remove_exact_duplicates(
    data=input_data,
    keep="first"  # first, last, none
)

# Remove near-duplicates (fuzzy matching)
fuzzy_deduplicated = remover.remove_fuzzy_duplicates(
    data=input_data,
    similarity_threshold=0.9,
    columns=["name", "description"],
    method="levenshtein"  # levenshtein, jaccard, cosine
)

Outlier Detection and Treatment¶

Identify and handle outliers:

from openmlcrawler.processing import OutlierDetector

detector = OutlierDetector()

# Statistical outlier detection
outliers = detector.detect_statistical_outliers(
    data=input_data,
    method="iqr",  # iqr, zscore, isolation_forest, local_outlier_factor
    columns=["price", "quantity"],
    threshold=1.5
)

# Handle outliers
cleaned_data = detector.handle_outliers(
    data=input_data,
    outliers=outliers,
    method="cap",  # cap, remove, impute
    cap_values={"price": [10, 1000], "quantity": [1, 100]}
)

Text Data Cleaning¶

Clean and normalize text data:

from openmlcrawler.processing import TextCleaner

text_cleaner = TextCleaner()

# Clean text data
cleaned_text = text_cleaner.clean_text(
    text_data=input_data["description"],
    operations=[
        "remove_html",
        "remove_urls",
        "remove_emails",
        "normalize_whitespace",
        "remove_punctuation",
        "lowercase",
        "remove_stopwords",
        "lemmatize"
    ],
    language="english"
)

Data Transformation¶

Data Normalization¶

Normalize data to common scales:

from openmlcrawler.processing import DataNormalizer

normalizer = DataNormalizer()

# Different normalization methods
normalized_data = normalizer.normalize(
    data=input_data,
    columns=["price", "quantity", "rating"],
    method="minmax",  # minmax, zscore, robust, maxabs, l1, l2
    feature_range=(0, 1)
)

Encoding Categorical Variables¶

Convert categorical variables to numerical:

from openmlcrawler.processing import CategoricalEncoder

encoder = CategoricalEncoder()

# Label encoding
label_encoded = encoder.label_encode(
    data=input_data,
    columns=["category", "status"]
)

# One-hot encoding
onehot_encoded = encoder.onehot_encode(
    data=input_data,
    columns=["category", "region"],
    drop_first=True,
    handle_unknown="ignore"
)

# Target encoding
target_encoded = encoder.target_encode(
    data=input_data,
    columns=["category"],
    target="sales",
    smoothing=1.0
)

Feature Engineering¶

Create new features from existing data:

from openmlcrawler.processing import FeatureEngineer

engineer = FeatureEngineer()

# Create temporal features
temporal_features = engineer.create_temporal_features(
    data=input_data,
    date_column="timestamp",
    features=[
        "hour", "day", "month", "year",
        "day_of_week", "week_of_year",
        "is_weekend", "is_holiday"
    ]
)

# Create interaction features
interaction_features = engineer.create_interaction_features(
    data=input_data,
    columns=["price", "quantity"],
    interactions=["multiply", "divide", "add", "subtract"]
)

# Create aggregation features
agg_features = engineer.create_aggregation_features(
    data=input_data,
    groupby_columns=["category", "region"],
    agg_functions=["mean", "std", "min", "max", "count"]
)

Data Reshaping¶

Reshape data for different analysis needs:

from openmlcrawler.processing import DataReshaper

reshaper = DataReshaper()

# Pivot data
pivoted_data = reshaper.pivot_data(
    data=input_data,
    index=["date", "category"],
    columns="region",
    values="sales",
    aggfunc="sum"
)

# Melt data
melted_data = reshaper.melt_data(
    data=input_data,
    id_vars=["id", "name"],
    value_vars=["q1_sales", "q2_sales", "q3_sales", "q4_sales"],
    var_name="quarter",
    value_name="sales"
)

# Transpose data
transposed_data = reshaper.transpose_data(
    data=input_data,
    index_column="id"
)

Data Enrichment¶

External Data Integration¶

Enrich data with external sources:

from openmlcrawler.processing import DataEnricher

enricher = DataEnricher()

# Enrich with geographic data
geo_enriched = enricher.enrich_geographic(
    data=input_data,
    location_column="address",
    enrichments=[
        "coordinates",
        "country_code",
        "timezone",
        "population_density"
    ]
)

# Enrich with demographic data
demo_enriched = enricher.enrich_demographic(
    data=input_data,
    location_data=geo_enriched,
    enrichments=[
        "age_distribution",
        "income_level",
        "education_level"
    ]
)

# Enrich with weather data
weather_enriched = enricher.enrich_weather(
    data=input_data,
    date_column="timestamp",
    location_column="coordinates",
    weather_features=[
        "temperature",
        "humidity",
        "precipitation",
        "wind_speed"
    ]
)

Derived Features¶

Create calculated features:

from openmlcrawler.processing import FeatureDeriver

deriver = FeatureDeriver()

# Create calculated features
derived_features = deriver.derive_features(
    data=input_data,
    derivations=[
        {
            "name": "profit_margin",
            "expression": "(revenue - cost) / revenue",
            "data_type": "float64"
        },
        {
            "name": "days_since_purchase",
            "expression": "current_date - purchase_date",
            "data_type": "int64"
        },
        {
            "name": "customer_segment",
            "expression": "CASE WHEN total_spent > 1000 THEN 'High' WHEN total_spent > 500 THEN 'Medium' ELSE 'Low' END",
            "data_type": "category"
        }
    ]
)

Data Linking¶

Link data across different sources:

from openmlcrawler.processing import DataLinker

linker = DataLinker()

# Fuzzy matching
linked_data = linker.fuzzy_link(
    left_data=customers,
    right_data=transactions,
    left_key="customer_name",
    right_key="buyer_name",
    similarity_threshold=0.8,
    method="levenshtein"
)

# Exact matching with preprocessing
exact_linked = linker.exact_link(
    left_data=products,
    right_data=inventory,
    left_key="product_id",
    right_key="sku",
    preprocess_keys=True
)

# Probabilistic linking
probabilistic_linked = linker.probabilistic_link(
    left_data=dataset1,
    right_data=dataset2,
    linking_variables=[
        {"name": "name", "method": "jaccard", "threshold": 0.7},
        {"name": "address", "method": "levenshtein", "threshold": 0.8}
    ],
    match_threshold=0.6
)

Data Quality Assurance¶

Quality Metrics¶

Monitor data quality metrics:

from openmlcrawler.processing import QualityAssurance

qa = QualityAssurance()

# Calculate quality metrics
quality_metrics = qa.calculate_metrics(
    data=processed_data,
    metrics=[
        "completeness",
        "accuracy",
        "consistency",
        "timeliness",
        "validity",
        "uniqueness"
    ]
)

# Generate quality report
quality_report = qa.generate_quality_report(
    metrics=quality_metrics,
    thresholds={
        "completeness": 0.95,
        "accuracy": 0.98,
        "consistency": 0.90
    }
)

Data Profiling¶

Profile data to understand its characteristics:

from openmlcrawler.processing import DataProfiler

profiler = DataProfiler()

# Generate data profile
profile = profiler.profile_data(
    data=input_data,
    include_statistics=True,
    include_distributions=True,
    include_correlations=True,
    sample_size=10000
)

# Detect data quality issues
issues = profiler.detect_issues(
    data=input_data,
    profile=profile,
    issue_types=[
        "missing_values",
        "outliers",
        "inconsistencies",
        "data_type_mismatches"
    ]
)

Automated Quality Checks¶

Set up automated quality monitoring:

from openmlcrawler.processing import QualityMonitor

monitor = QualityMonitor()

# Define quality rules
quality_rules = [
    {
        "name": "no_null_ids",
        "check": "data['id'].notnull().all()",
        "severity": "error"
    },
    {
        "name": "price_range",
        "check": "(data['price'] >= 0).all() and (data['price'] <= 10000).all()",
        "severity": "warning"
    },
    {
        "name": "date_validity",
        "check": "pd.to_datetime(data['date'], errors='coerce').notnull().all()",
        "severity": "error"
    }
]

# Run quality checks
check_results = monitor.run_checks(
    data=input_data,
    rules=quality_rules,
    fail_fast=False
)

# Alert on quality issues
monitor.alert_on_issues(
    results=check_results,
    alert_methods=["email", "slack", "dashboard"],
    alert_thresholds={"error": 0, "warning": 5}
)

Pipeline Orchestration¶

Workflow Definition¶

Define complex processing workflows:

from openmlcrawler.processing import PipelineOrchestrator

orchestrator = PipelineOrchestrator()

# Define processing workflow
workflow = {
    "name": "customer_analytics_pipeline",
    "steps": [
        {
            "name": "ingest_customer_data",
            "type": "ingestion",
            "sources": ["crm_database", "web_analytics"],
            "output": "raw_customers"
        },
        {
            "name": "validate_customer_data",
            "type": "validation",
            "input": "raw_customers",
            "schema": "customer_schema.json",
            "output": "validated_customers"
        },
        {
            "name": "clean_customer_data",
            "type": "cleaning",
            "input": "validated_customers",
            "operations": ["remove_duplicates", "handle_missing"],
            "output": "clean_customers"
        },
        {
            "name": "enrich_customer_data",
            "type": "enrichment",
            "input": "clean_customers",
            "enrichments": ["geographic", "demographic"],
            "output": "enriched_customers"
        }
    ],
    "error_handling": "continue",
    "logging": "detailed"
}

# Execute workflow
result = orchestrator.execute_workflow(workflow)

Parallel Processing¶

Process data in parallel for better performance:

from openmlcrawler.processing import ParallelProcessor

parallel_processor = ParallelProcessor()

# Process data in parallel
results = parallel_processor.process_parallel(
    data=input_data,
    operations=[
        {"name": "clean_text", "function": clean_text_columns},
        {"name": "normalize_numeric", "function": normalize_numeric_columns},
        {"name": "encode_categorical", "function": encode_categorical_columns}
    ],
    num_workers=4,
    chunk_size=1000
)

Conditional Processing¶

Apply different processing based on conditions:

from openmlcrawler.processing import ConditionalProcessor

conditional_processor = ConditionalProcessor()

# Define conditional processing rules
rules = [
    {
        "condition": "data['data_type'] == 'sales'",
        "operations": [
            "validate_sales_schema",
            "clean_sales_data",
            "aggregate_sales"
        ]
    },
    {
        "condition": "data['data_type'] == 'inventory'",
        "operations": [
            "validate_inventory_schema",
            "update_stock_levels",
            "calculate_reorder_points"
        ]
    },
    {
        "condition": "data['source'] == 'api'",
        "operations": [
            "validate_api_response",
            "flatten_nested_data",
            "standardize_formats"
        ]
    }
]

# Apply conditional processing
processed_data = conditional_processor.apply_conditional_processing(
    data=input_data,
    rules=rules
)

Configuration and Monitoring¶

Pipeline Configuration¶

Configure pipeline behavior:

data_processing:
  pipeline:
    name: "main_processing_pipeline"
    version: "1.0.0"
    stages:
      - ingestion
      - validation
      - cleaning
      - transformation
      - enrichment
      - quality_assurance

  validation:
    strict_mode: true
    fail_on_error: false
    error_threshold: 0.05

  cleaning:
    missing_value_strategy: "interpolate"
    outlier_method: "iqr"
    duplicate_handling: "keep_first"

  transformation:
    normalization_method: "zscore"
    encoding_method: "onehot"
    feature_engineering: true

  monitoring:
    enable_metrics: true
    log_level: "INFO"
    alert_on_errors: true

Performance Monitoring¶

Monitor pipeline performance:

from openmlcrawler.processing import PipelineMonitor

monitor = PipelineMonitor()

# Monitor pipeline execution
execution_metrics = monitor.monitor_execution(
    pipeline_result=result,
    metrics=[
        "execution_time",
        "memory_usage",
        "cpu_usage",
        "error_rate",
        "throughput"
    ]
)

# Generate performance report
performance_report = monitor.generate_performance_report(
    metrics=execution_metrics,
    time_range="last_24h",
    include_charts=True
)

# Set up alerts
monitor.setup_alerts(
    conditions=[
        {"metric": "error_rate", "operator": ">", "value": 0.1, "action": "alert"},
        {"metric": "execution_time", "operator": ">", "value": 3600, "action": "notify"}
    ]
)

Best Practices¶

Data Quality¶

Validate Early: Implement validation at the earliest possible stage
Fail Fast: Stop processing when critical data quality issues are detected
Monitor Continuously: Set up continuous monitoring of data quality metrics
Document Issues: Maintain detailed logs of data quality issues and resolutions
Automate Remediation: Implement automated fixes for common data quality issues

Performance Optimization¶

Parallel Processing: Use parallel processing for independent operations
Memory Management: Monitor and optimize memory usage for large datasets
Caching: Cache intermediate results to avoid redundant computations
Batch Processing: Process data in optimal batch sizes
Resource Allocation: Allocate appropriate resources based on data volume

Error Handling¶

Graceful Degradation: Continue processing when non-critical errors occur
Detailed Logging: Log all errors with sufficient context for debugging
Retry Logic: Implement retry logic for transient failures
Circuit Breakers: Use circuit breakers to prevent cascade failures
Fallback Strategies: Define fallback strategies for critical failures

Maintainability¶

Modular Design: Break down complex pipelines into smaller, manageable components
Configuration Management: Externalize configuration to make pipelines adaptable
Version Control: Version control pipeline definitions and configurations
Documentation: Document pipeline logic, dependencies, and maintenance procedures
Testing: Implement comprehensive testing for pipeline components

Troubleshooting¶

Common Issues¶

Memory Errors¶

Error: Out of memory during processing
Solution: Reduce batch sizes, use streaming processing, or increase memory allocation

Data Type Mismatches¶

Error: Data type validation failed
Solution: Check source data types, update type mappings, or implement type conversion

Performance Degradation¶

Error: Pipeline execution time increased significantly
Solution: Profile pipeline performance, optimize bottlenecks, or scale resources

Data Quality Issues¶

Error: Data quality metrics below threshold
Solution: Review data sources, update validation rules, or implement data cleaning