• Data Pipeline Architectures – Different approaches to moving data
• Change Data Capture (CDC) – Techniques for identifying changed data
• Data Quality Management – Validation, cleansing, and monitoring
• Metadata Management – Tracking data lineage and documentation
• Error Handling – Strategies for managing data exceptions

In the evolving landscape of data engineering, the processes of extracting, transforming, and loading data (ETL) or extracting, loading, and transforming data (ELT) form the backbone of any effective data integration strategy. These processes are not merely technical implementations but architectural decisions that significantly impact data quality, system performance, maintenance effort, and ultimately, the business value derived from data.

As organizations face increasingly complex data landscapes—with diverse sources, growing volumes, and demanding analytical requirements—understanding and applying proven design patterns becomes essential for building robust, scalable, and maintainable data pipelines. This article explores five fundamental ETL/ELT design patterns that address key challenges in modern data integration architectures.

The architecture of your data pipeline is a foundational decision that shapes all subsequent implementation details. Modern data environments offer several architectural patterns, each with distinct advantages for specific scenarios.

Batch processing remains the workhorse of data integration, processing data in scheduled intervals rather than continuously.

• Scheduled Execution: Runs at predetermined intervals (hourly, daily, weekly)
• Complete Dataset Processing: Often processes entire datasets or identified changes
• Resource Efficiency: Optimizes resource usage during off-peak hours
• Predictable Loads: Creates manageable, predictable system loads
• Transaction Support: Provides strong transactional guarantees

Traditional ETL Flow

Source Systems → Extraction → Staging Area → Transformation → Data Warehouse → Reporting Layer

ELT with Modern Data Platforms

Source Systems → Extraction → Raw Data Layer → Transformation (in-database) → Consumption Layer

• Implement clear dependency management between jobs
• Design for restartability and idempotence
• Optimize for parallel processing where possible
• Implement comprehensive logging and monitoring
• Consider incremental processing for large datasets

• Nightly financial reconciliation processes
• Weekly or monthly reporting cycles
• Large-scale data transformations
• Complex integration processes requiring coordination
• Systems with limited source availability windows

Streaming architectures process data continuously as events occur, enabling near real-time data integration and analytics.

• Continuous Processing: Handles data as it arrives
• Event-Driven Design: Responds to data events rather than schedules
• Low Latency: Minimizes delay between data creation and availability
• Scalable Throughput: Handles varying data volumes
• Stateful Processing: Maintains context across events when needed

Stream Processing Pipeline

Source Systems → Event Broker (Kafka/Kinesis) → Stream Processor (Spark/Flink) → Data Storage → Real-time Analytics

Lambda Architecture

                        → Speed Layer (Stream Processing) → Real-time Views   → 
Source Systems → Ingestion                                                      → Serving Layer → Applications
                        → Batch Layer (Batch Processing) → Batch Views       → 

• Design for fault tolerance and exactly-once processing
• Implement backpressure handling mechanisms
• Consider state management carefully
• Plan for late-arriving data
• Design schema evolution strategy

• Real-time dashboards and monitoring
• Fraud detection systems
• IoT data processing
• User behavior analytics
• Operational intelligence

Micro-batch architectures strike a balance between batch and streaming approaches, processing data in small, frequent batches.

• Short-Interval Processing: Runs every few minutes
• Bounded Dataset Size: Processes small chunks of data
• Simplified Implementation: Easier to implement than true streaming
• Near Real-Time: Approaches streaming latency
• Batch Processing Benefits: Maintains transaction guarantees

Micro-Batch Pipeline

Source Systems → Change Detection → Small Batch Extraction → Quick Transformation → Target Storage → Analytics

Structured Streaming (Spark)

Source Systems → Micro-Batch Ingestion → Structured Streaming Processing → Delta Tables → Applications

• Optimize batch size for throughput vs. latency
• Implement efficient change detection
• Design for parallel batch processing
• Ensure proper batch identification and tracking
• Consider state management across micro-batches

• Near real-time dashboards with minute-level freshness
• Systems with moderate latency requirements
• Applications needing transaction guarantees
• Scenarios with complex transformations
• Environments with limited streaming infrastructure

Hybrid architectures combine multiple approaches to optimize for different data characteristics and requirements.

• Mixed Processing Models: Combines batch, micro-batch, and streaming
• Workload-Optimized Design: Matches architecture to specific data needs
• Flexible Implementation: Adapts to varying business requirements
• Progressive Adoption: Enables phased implementation
• Balanced Resource Usage: Optimizes infrastructure utilization

Kappa Architecture

Source Systems → Event Broker → Stream Processing → Storage Layer → Batch Views & Real-time Views

Unified Batch and Stream

                                 → Batch Processing → 
Source Systems → Data Collection                      → Unified Storage → Applications
                                 → Stream Processing → 

• Clearly define criteria for routing to batch vs. stream
• Implement unified metadata management
• Ensure consistent data quality across flows
• Design for interoperability between components
• Create unified monitoring and observability

• Organizations with diverse data latency requirements
• Environments transitioning from batch to streaming
• Complex systems with varied data sources
• Applications with both historical and real-time needs
• Scenarios requiring flexibility in processing models

Orchestration-centric architectures focus on workflow management, dependencies, and process coordination rather than the specific processing model.

• Workflow Management: Centers on orchestration tools
• Dependency Handling: Manages complex job dependencies
• Process Coordination: Synchronizes disparate systems
• Visibility: Provides clear process monitoring
• Resilience: Handles failures gracefully

DAG-Based Orchestration

                                   → Extract Job A → Transform Job A → 
Orchestrator (Airflow/Dagster) → Extract Job B → Transform Job B → Load Jobs → Validation → Reporting
                                   → Extract Job C → Transform Job C → 

Event-Driven Orchestration

Source Systems → Event Detection → Workflow Trigger → Orchestration Platform → Dynamic Pipeline Execution

• Design granular, reusable tasks
• Implement comprehensive error handling and recovery
• Create clear visualization of process flows
• Ensure proper resource management
• Implement parameterized, templated workflows

• Complex enterprise data pipelines
• Processes with intricate dependencies
• Multi-system integration scenarios
• Environments requiring strong governance
• Operations needing detailed audit trails

Change Data Capture (CDC) represents a crucial pattern for efficiently identifying and processing only the data that has changed since the previous extraction, dramatically reducing processing overhead and enabling near real-time data integration.

Log-based CDC directly accesses the database transaction log to capture changes.

• Low Impact: Minimal performance impact on source systems
• Complete Capture: Records all changes including deletes
• Chronological Order: Preserves sequence of changes
• Transactional Integrity: Maintains transaction boundaries
• Minimal Latency: Near real-time change detection

Database-Native CDC

Database Transaction Logs → CDC Reader → Change Events → Change Data Storage → Transformation & Loading

Third-Party CDC Tools

Database → CDC Tool (Debezium/Attunity) → Change Stream → Kafka/Kinesis → Stream Processing → Target

• Ensure proper log retention configuration
• Implement checkpointing for restart capability
• Consider impact of schema changes
• Plan for high availability of CDC components
• Design for exactly-once delivery semantics

• Real-time data synchronization
• Operational data stores
• Data warehouse incremental loads
• Microservice data synchronization
• Cross-database replication

Query-based CDC identifies changes by comparing current data with previous extracts or using change tracking columns.

• Universal Applicability: Works with any queryable source
• Simpler Implementation: No specialized tools required
• Flexible Granularity: Configurable change detection criteria
• Selective Capture: Can focus on specific changes of interest
• Limited Source Impact: Minimal source system configuration

Timestamp-Based Detection

-- Extract changed records based on last_modified timestamp
SELECT * FROM source_table 
WHERE last_modified_timestamp > :last_extraction_timestamp

Version-Based Detection

-- Extract changed records based on version number
SELECT * FROM source_table 
WHERE version_number > :last_processed_version

Checksum Comparison

-- Compare current record hash with previously stored hash
SELECT * FROM source_table 
WHERE MD5(col1, col2, col3...) <> :stored_checksum_for_record

• Ensure reliable change tracking columns in source
• Implement efficient indexing on tracking columns
• Consider handling of deleted records
• Design for handling missed extraction cycles
• Implement change conflict resolution strategies

• Sources without log access
• Systems without native CDC support
• Selective data synchronization
• Less frequent update requirements
• Simple integration scenarios

Trigger-based CDC uses database triggers to capture and record changes when they occur.

• Immediate Capture: Records changes at transaction time
• Complete Control: Customizable change tracking logic
• Source System Implementation: Operates within the source database
• Detailed Change Information: Can capture old and new values
• Self-Contained: No external dependencies

Change Tracking Tables

-- Create change tracking table
CREATE TABLE customer_changes (
    change_id SERIAL PRIMARY KEY,
    operation VARCHAR(10), -- INSERT, UPDATE, DELETE
    change_timestamp TIMESTAMP,
    customer_id INT,
    column_name VARCHAR(50),
    old_value TEXT,
    new_value TEXT
);

-- Create trigger
CREATE TRIGGER customer_change_trigger
AFTER INSERT OR UPDATE OR DELETE ON customers
FOR EACH ROW EXECUTE PROCEDURE track_customer_changes();

Change Data Queue

Database Trigger → Change Message → Message Queue → CDC Consumer → Processing Pipeline

• Minimize trigger complexity to reduce performance impact
• Implement efficient change storage mechanisms
• Consider transaction volume when designing triggers
• Plan for trigger maintenance during schema changes
• Design for minimal contention with operational transactions

• Systems with moderate transaction volumes
• Environments requiring custom change logic
• Scenarios needing both old and new values
• Applications without alternative CDC options
• On-premises legacy databases

Event-sourcing CDC treats changes as immutable events in an event stream, providing a complete audit history.

• Event-First Design: All changes are immutable events
• Complete History: Maintains full audit trail of changes
• Temporal Queries: Enables point-in-time reconstruction
• Domain-Oriented: Aligned with domain-driven design
• Decoupled Architecture: Separates change production from consumption

Event Store Pattern

Application → Events → Event Store → Event Consumers → Projections/Views

Event-Sourced Microservices

Microservice Commands → Domain Events → Event Log → CDC Connector → Data Warehouse

• Design clear event schemas with versioning
• Implement efficient event storage and retrieval
• Consider event partitioning for scalability
• Plan for event schema evolution
• Design appropriate event granularity

• Systems requiring complete audit history
• Domain-driven design implementations
• Complex event processing scenarios
• Applications with temporal query needs
• Microservice architectures

Data quality management within ETL/ELT processes ensures that data remains accurate, consistent, and reliable throughout the integration pipeline.

The validation-first pattern prioritizes data validation before any transformation or loading, creating a quality gate early in the process.

• Early Detection: Identifies issues before processing
• Fail-Fast Approach: Prevents propagation of bad data
• Comprehensive Validation: Applies multiple quality rules
• Clear Quality Standards: Explicit acceptance criteria
• Isolated Validation: Separates validation from transformation

Rule-Based Validation

# Pseudo-code for validation rules
validation_rules = [
    {"rule": "not_null", "columns": ["customer_id", "order_date", "amount"]},
    {"rule": "unique", "columns": ["order_id"]},
    {"rule": "range", "column": "amount", "min": 0, "max": 1000000},
    {"rule": "pattern", "column": "email", "regex": r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"},
    {"rule": "referential", "column": "customer_id", "ref_table": "customers", "ref_column": "id"}
]

# Apply validation before transformation
validation_results = validate_data(source_data, validation_rules)
if validation_results.has_failures():
    handle_validation_failures(validation_results)
else:
    proceed_with_transformation(source_data)

Schema Validation

# Validate against expected schema
expected_schema = {
    "customer_id": {"type": "integer", "required": True},
    "order_date": {"type": "date", "required": True},
    "amount": {"type": "decimal", "required": True, "min": 0},
    "status": {"type": "string", "required": True, "allowed": ["pending", "shipped", "delivered", "canceled"]}
}

schema_validation = validate_schema(source_data, expected_schema)
if schema_validation.is_valid:
    proceed_with_pipeline(source_data)
else:
    reject_data(source_data, schema_validation.errors)

• Implement comprehensive validation rule libraries
• Design clear validation failure handling
• Create detailed validation reports
• Consider performance impact for large datasets
• Implement progressive validation levels

• Critical business data pipelines
• Regulated data environments
• Financial and healthcare data
• Customer-facing analytics
• Multi-source integration

The in-pipeline quality pattern integrates data quality checks throughout the transformation process, addressing quality progressively.

• Continuous Quality Checks: Throughout the pipeline
• Contextual Validation: Rules based on transformation stage
• Progressive Refinement: Quality improves at each stage
• Transformation-Specific Checks: Tailored to each step
• Granular Issue Detection: Pinpoints where problems occur

Quality Checkpoints

Extract → Quality Check → Transform Phase 1 → Quality Check → Transform Phase 2 → Quality Check → Load

Quality-Aware Transformations

# Pseudo-code for quality-aware transformation
def transform_customer_data(data):
    # Initial validation
    validate_required_fields(data)
    
    # First transformation with quality check
    transformed_data = standardize_names(data)
    validate_standardized_names(transformed_data)
    
    # Second transformation with quality check
    enriched_data = add_geographic_data(transformed_data)
    validate_geographic_enrichment(enriched_data)
    
    # Final validation before output
    validate_output_requirements(enriched_data)
    return enriched_data

• Design checkpoints based on data criticality
• Implement appropriate error thresholds for each stage
• Create detailed logging of quality metrics
• Consider performance impact of frequent checks
• Design recovery mechanisms for each checkpoint

• Complex multi-stage transformations
• Data with varying quality requirements
• Enrichment-heavy pipelines
• Machine learning data preparation
• Processes with multiple quality dimensions

The data cleansing pattern focuses on fixing data quality issues rather than just identifying them, actively improving data quality during processing.

• Active Remediation: Fixes issues rather than rejecting data
• Standardization Rules: Normalizes data formats
• Enrichment Logic: Adds missing information
• Error Correction: Fixes identifiable mistakes
• Quality Tracking: Measures improvement metrics

Standard Cleansing Pipeline

Raw Data → Deduplication → Format Standardization → Value Normalization → Enrichment → Validated Clean Data

Rule-Based Cleansing

# Pseudo-code for cleansing rules
cleansing_rules = [
    {"type": "standardize_case", "columns": ["first_name", "last_name"], "case": "title"},
    {"type": "remove_whitespace", "columns": ["email", "phone"]},
    {"type": "format_phone", "column": "phone", "format": "(XXX) XXX-XXXX"},
    {"type": "standardize_address", "columns": ["address", "city", "state", "zip"]},
    {"type": "default_value", "column": "country", "default": "USA", "condition": "is_null"}
]

cleansed_data = apply_cleansing_rules(raw_data, cleansing_rules)

• Document all cleansing transformations
• Implement before/after quality metrics
• Consider preserving original values
• Design for maintainable cleansing rules
• Create feedback loops for rule improvement

• Customer data integration
• Address standardization
• Product data harmonization
• Marketing data preparation
• Master data management

The quality monitoring pattern focuses on ongoing observation of data quality metrics, enabling trend analysis and proactive quality management.

• Continuous Measurement: Tracks quality over time
• Trend Analysis: Identifies declining quality patterns
• Alerting Capabilities: Notifies on quality deviations
• Dimensional Metrics: Measures multiple quality aspects
• Visualization: Presents quality metrics clearly

Quality Dashboard

ETL Process → Quality Metrics Collection → Time Series Database → Quality Dashboard → Alerts

Automated Quality Reporting

# Pseudo-code for quality metrics collection
quality_metrics = {
    "completeness": calculate_completeness(data),
    "accuracy": calculate_accuracy(data, reference_data),
    "consistency": calculate_consistency(data),
    "timeliness": calculate_timeliness(data, current_time),
    "uniqueness": calculate_uniqueness(data)
}

store_quality_metrics(quality_metrics, dataset_id, execution_time)
check_for_alerts(quality_metrics, alert_thresholds)

• Establish clear quality baseline metrics
• Implement trend analysis algorithms
• Design actionable alerting thresholds
• Create informative quality dashboards
• Establish quality governance processes

• Ongoing data governance
• Data quality service level agreements
• Regulatory compliance reporting
• Data quality improvement initiatives
• Source system quality monitoring

The quality-as-code pattern treats data quality rules as versioned, tested code assets integrated into the development lifecycle.

• Versioned Quality Rules: Managed in source control
• Testable Specifications: Can be validated with test data
• Deployment Pipeline: Follows software delivery practices
• Reusable Components: Quality libraries and functions
• Documentation: Self-documenting quality requirements

Quality Rule Repository

Quality Rule Code → Unit Tests → CI/CD Pipeline → Deployment → ETL Processes

Declarative Quality Definitions

# YAML quality rule definition
dataset: customer_orders
rules:
  - name: valid_order_amount
    description: "Order amount must be positive and within reasonable range"
    type: range
    column: order_amount
    min: 0.01
    max: 50000
    severity: error
    
  - name: valid_order_date
    description: "Order date must not be in the future"
    type: comparison
    column: order_date
    operator: less_than_or_equal
    value: CURRENT_DATE
    severity: error

• Implement quality rule testing
• Create clear versioning strategy
• Design for rule reusability
• Establish quality rule governance
• Document business justification for rules

• Enterprise data platforms
• Cross-team data integration
• DevOps-oriented organizations
• Regulatory environments
• Complex data ecosystems

Effective metadata management is essential for understanding, governing, and maintaining ETL/ELT processes, providing critical context about data’s origin, transformations, and usage.

The technical metadata capture pattern focuses on automatically collecting and maintaining structural and operational metadata throughout the data pipeline.

• Automated Collection: Minimal manual documentation
• Schema Tracking: Captures data structures and changes
• Process Metadata: Records execution details
• System Information: Documents technical environment
• Change History: Maintains technical evolution

Schema Registry Integration

Source Systems → Schema Registry → ETL Processes → Target Systems → Schema History

Pipeline Metadata Collection

# Pseudo-code for capturing technical metadata
def capture_technical_metadata(job_execution):
    metadata = {
        "job_id": job_execution.id,
        "source_schema": extract_schema(job_execution.source),
        "target_schema": extract_schema(job_execution.target),
        "row_counts": {
            "source": count_rows(job_execution.source),
            "target": count_rows(job_execution.target)
        },
        "execution_environment": get_environment_details(),
        "performance_metrics": get_performance_metrics(job_execution)
    }
    store_technical_metadata(metadata)

• Implement automated schema detection
• Design for historical schema tracking
• Create clear technical metadata visualization
• Establish metadata versioning strategy
• Integrate with data catalog systems

• Large data ecosystems
• Complex data pipelines
• Environments with frequent schema changes
• Multi-team data engineering
• Regulatory compliance requirements

The lineage tracking pattern focuses on documenting the complete data journey from source to consumption, capturing transformations and dependencies along the way.

• End-to-End Tracking: From source to consumption
• Transformation Documentation: Records data changes
• Process Dependencies: Maps relationships between steps
• Impact Analysis: Enables change effect assessment
• Backward/Forward Tracing: Bidirectional lineage

Explicit Lineage Registration

# Pseudo-code for lineage registration
lineage_manager.register_source(source_table, source_columns)
lineage_manager.register_transformation(
    input_dataset=source_table, 
    output_dataset=target_table,
    transformation_type="join",
    transformation_details={"join_keys": ["customer_id"], "join_type": "left"}
)
lineage_manager.register_columns_mapping({
    "source.first_name": "target.customer_first_name",
    "source.last_name": "target.customer_last_name",
    "transformation.full_address": "target.customer_address"
})

Automated Lineage Extraction

SQL Parsing → Code Analysis → Lineage Graph Construction → Lineage Database → Lineage Visualization

• Design for appropriate lineage granularity
• Implement both dataset and field-level lineage
• Create visual lineage representation
• Establish lineage-based impact analysis
• Integrate with governance processes

• Regulatory compliance reporting
• Data governance initiatives
• Complex transformation processes
• Change management support
• Data trustworthiness assessment

The business metadata integration pattern focuses on connecting technical processes with business context, creating meaningful documentation for data consumers.

• Business Definitions: Links technical elements to business terms
• Data Ownership: Documents responsible teams/individuals
• Usage Context: Describes intended data usage
• Quality Expectations: Documents business quality requirements
• Business Rules: Documents calculation logic

Business Glossary Integration

Business Glossary → Term Association → ETL Processes → Enriched Data Catalog

Metadata Enrichment Framework

# Pseudo-code for business metadata enrichment
def enrich_with_business_metadata(technical_dataset):
    business_metadata = {
        "business_definition": glossary.get_definition(technical_dataset.name),
        "business_owner": ownership_registry.get_owner(technical_dataset.name),
        "data_domain": domain_registry.get_domain(technical_dataset.name),
        "confidentiality": security_registry.get_classification(technical_dataset.name),
        "business_rules": calculation_registry.get_rules(technical_dataset.name)
    }
    technical_dataset.add_business_metadata(business_metadata)
    return technical_dataset

• Establish clear business metadata governance
• Implement metadata review processes
• Create business-friendly metadata interfaces
• Design for metadata reuse across systems
• Establish metadata quality measures

• Self-service analytics environments
• Cross-functional data teams
• Data democratization initiatives
• Business-IT alignment efforts
• Data literacy programs

The operational metadata management pattern focuses on tracking the runtime behavior and performance of data pipelines, enabling operational monitoring and optimization.

• Execution Tracking: Records run details and status
• Performance Metrics: Captures processing times and resources
• Data Volume Metrics: Tracks record counts and volumes
• Dependency Tracking: Monitors job relationships
• Historical Execution Data: Maintains performance history

Job Registry Pattern

ETL Jobs → Job Registry → Execution Logs → Operational Dashboard → Alerts

Operational Metrics Collection

# Pseudo-code for operational metadata collection
def collect_operational_metadata(job_execution):
    operational_metadata = {
        "job_id": job_execution.id,
        "start_time": job_execution.start_time,
        "end_time": job_execution.end_time,
        "status": job_execution.status,
        "records_processed": job_execution.record_count,
        "data_volume_bytes": job_execution.data_volume,
        "error_count": job_execution.error_count,
        "resource_utilization": {
            "cpu": job_execution.cpu_metrics,
            "memory": job_execution.memory_metrics,
            "io": job_execution.io_metrics
        }
    }
    store_operational_metadata(operational_metadata)

• Implement comprehensive execution logging
• Design historical performance tracking
• Create operational dashboards
• Establish performance baselines
• Integrate with monitoring systems

• Production data pipelines
• SLA-driven environments
• Resource optimization efforts
• Operational troubleshooting
• Capacity planning

The metadata-driven execution pattern uses metadata to dynamically control ETL/ELT execution, enabling flexible, configurable processing logic.

• Configuration-Based Processing: Minimal hardcoded logic
• Dynamic Pipeline Generation: Created from metadata
• Centralized Control: Single point of process management
• Reusable Patterns: Template-based implementation
• Reduced Code Duplication: Generic processing logic

Declarative Pipeline Definition

# YAML pipeline definition
pipeline:
  name: customer_integration
  source:
    type: database
    connection: crm_system
    query: "SELECT * FROM customers WHERE last_modified > :last_run_date"
  transformations:
    - type: standardize_names
      columns: [first_name, last_name]
    - type: address_validation
      columns: [address, city, state, zip]
    - type: deduplication
      keys: [email, phone]
  target:
    type: data_warehouse
    connection: enterprise_dw
    table: dim_customer
    load_type: merge
    keys: [customer_id]

Metadata Repository Driven Execution

Metadata Repository → Pipeline Generator → Generated ETL Code → Execution Engine → Results

• Implement metadata validation
• Design for metadata versioning
• Create clear metadata authoring interfaces
• Establish metadata-driven testing
• Document metadata schemas comprehensively

• Large-scale data integration
• Multi-pattern processing requirements
• Frequent pipeline changes
• Standardized integration patterns
• Cross-team collaboration

Robust error handling is critical for building resilient data pipelines that can gracefully handle exceptions without data loss or pipeline failures.

The fail-fast pattern prioritizes early detection and immediate failure when errors occur, preventing downstream issues and clearly identifying problems.

• Early Validation: Checks before significant processing
• Immediate Termination: Stops on error detection
• Clear Error Reporting: Provides detailed error information
• No Partial Processing: Prevents partial data loading
• Transactional Integrity: Maintains data consistency

Pre-Execution Validation

# Pseudo-code for fail-fast validation
def process_data_fail_fast(data_source):
    # Validate before processing
    validation_result = validate_source_data(data_source)
    
    if not validation_result.is_valid:
        raise ProcessingError(f"Validation failed: {validation_result.errors}")
    
    # Process only if validation passes
    extracted_data = extract_data(data_source)
    transformed_data = transform_data(extracted_data)
    load_data(transformed_data)

Transaction Boundary Control

# Pseudo-code for transaction control
with database.transaction() as tx:
    try:
        # All operations must succeed or none will
        validate_data(source_data)
        transformed_data = transform_data(source_data)
        load_data(transformed_data)
        # Commit happens automatically if we reach the end
    except Exception as e:
        # Any exception triggers rollback
        log_error(f"Processing failed: {str(e)}")
        tx.rollback()
        raise

• Implement comprehensive pre-execution validation
• Create detailed error reporting
• Design clean failure states
• Establish clear retry policies
• Implement transaction management

• Financial data processing
• Atomic data updates
• Highly interdependent pipelines
• Critical business data
• Scenarios requiring strict data consistency

The dead letter queue pattern captures and stores records that failed processing, allowing the pipeline to continue while preserving problematic data for later resolution.

• Isolation of Failures: Separates bad records
• Continued Processing: Pipeline proceeds despite errors
• Error Preservation: Maintains problematic records
• Later Resolution: Enables offline error handling
• Error Context: Captures failure information

Side-Channel Error Routing

Source Data → Processing Pipeline → Success Path → Target System
                     ↓
                Error Handler → Dead Letter Storage → Error Resolution

Error Collection Implementation

# Pseudo-code for dead letter queue
def process_batch_with_dlq(records):
    results = []
    errors = []
    
    for record in records:
        try:
            processed_record = transform_record(record)
            results.append(processed_record)
        except Exception as e:
            # Capture record and error

        error_entry = {
            "record": record,
            "error": str(e),
            "timestamp": current_timestamp(),
            "processing_context": get_context_information()
        }
        errors.append(error_entry)

# Store successful records
if results:
    store_processed_records(results)

# Store error records
if errors:
    store_in_dead_letter_queue(errors)

return len(results), len(errors)

##### Best Practices:
- Design comprehensive error context capture
- Implement error categorization
- Create error resolution workflows
- Establish error monitoring and alerting
- Design for error reprocessing

##### Ideal Use Cases:
- High-volume data pipelines
- Systems with variable data quality
- Non-critical data processing
- Streaming data integration
- IoT data processing

#### Circuit Breaker Pattern

The circuit breaker pattern monitors failure rates and temporarily suspends operations when failures exceed thresholds, preventing system overload and allowing recovery.

##### Key Characteristics:
- **Failure Rate Monitoring**: Tracks error percentages
- **Automatic Suspension**: Halts operation when thresholds exceeded
- **Recovery Testing**: Periodically tests system readiness
- **Gradual Recovery**: Carefully resumes normal operation
- **State Transparency**: Provides clear status visibility

##### Implementation Approaches:

**State Machine Implementation**
```python
# Pseudo-code for circuit breaker
class CircuitBreaker:
    def __init__(self, failure_threshold=0.5, recovery_timeout=300):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN
        self.failures = 0
        self.successes = 0
        self.total_calls = 0
        self.last_failure_time = None
    
    def execute(self, operation):
        if self.state == "OPEN":
            # Check if recovery timeout has elapsed
            if (current_time() - self.last_failure_time) > self.recovery_timeout:
                self.state = "HALF_OPEN"
                self.successes = 0
                self.failures = 0
            else:
                raise CircuitOpenError("Circuit breaker is open")
        
        try:
            result = operation()
            self._record_success()
            return result
        except Exception as e:
            self._record_failure()
            raise e
    
    def _record_success(self):
        self.successes += 1
        self.total_calls += 1
        
        if self.state == "HALF_OPEN" and self.successes >= 3:
            # Successfully recovered
            self.state = "CLOSED"
            self.reset_counts()
    
    def _record_failure(self):
        self.failures += 1
        self.total_calls += 1
        self.last_failure_time = current_time()
        
        failure_rate = self.failures / self.total_calls if self.total_calls > 0 else 0
        
        if (self.state == "CLOSED" and failure_rate >= self.failure_threshold) or self.state == "HALF_OPEN":
            self.state = "OPEN"

Service-Level Circuit Breaking

Source System → Circuit Breaker → ETL Pipeline → Target System
                      ↓
              Monitoring System ← Alerting

• Tune thresholds based on system characteristics
• Implement graduated recovery mechanisms
• Create comprehensive circuit breaker monitoring
• Design for multiple granularity levels
• Establish alert mechanisms for state changes

• External system dependencies
• APIs with usage limits
• Fragile source systems
• Network-dependent operations
• Cascading failure prevention

The retry pattern automatically attempts failed operations multiple times, handling transient errors gracefully without manual intervention.

• Automatic Reattempts: Tries operations multiple times
• Backoff Strategies: Increasingly longer waits between attempts
• Retry Limiting: Caps maximum retry attempts
• Error Differentiation: Distinguishes retriable errors
• Context Preservation: Maintains operation state between attempts

Exponential Backoff Retry

# Pseudo-code for exponential backoff retry
def retry_with_backoff(operation, max_retries=3, initial_delay=1):
    retries = 0
    while True:
        try:
            return operation()
        except RetriableError as e:
            retries += 1
            if retries > max_retries:
                raise MaxRetriesExceededError(f"Operation failed after {max_retries} attempts") from e
            
            # Calculate backoff delay (1s, 2s, 4s, 8s, etc.)
            delay = initial_delay * (2 ** (retries - 1))
            
            log_warning(f"Operation failed, retrying in {delay}s. Attempt {retries}/{max_retries}")
            sleep(delay)
        except NonRetriableError as e:
            # Immediately fail on non-retriable errors
            log_error(f"Non-retriable error occurred: {str(e)}")
            raise

Retry with Circuit Breaker

# Pseudo-code combining retry and circuit breaker
def process_with_resilience(operation):
    circuit = CircuitBreaker(failure_threshold=0.3, recovery_timeout=60)
    
    def retriable_operation():
        return circuit.execute(operation)
    
    return retry_with_backoff(retriable_operation, max_retries=3, initial_delay=2)

• Classify errors as retriable vs. non-retriable
• Implement appropriate backoff strategies
• Design for idempotent operations
• Create detailed retry logging
• Establish maximum retry thresholds

• Network operations
• Database connections
• API calls
• Resource contention scenarios
• Cloud service interactions

The compensating transaction pattern handles failures in distributed operations by executing reverse operations to maintain system consistency.

• Undo Operations: Reverses completed steps on failure
• Distributed Consistency: Maintains system-wide integrity
• Sequence Tracking: Monitors operation order for reversal
• Stateful Management: Tracks transaction progress
• Saga Implementation: Follows saga pattern principles

Step-by-Step Compensation

# Pseudo-code for compensating transaction
def execute_with_compensation(steps):
    completed_steps = []
    
    try:
        # Execute all steps in sequence
        for step in steps:
            step.execute()
            completed_steps.append(step)
    except Exception as e:
        # Failure occurred, compensate for completed steps in reverse order
        for completed_step in reversed(completed_steps):
            try:
                completed_step.compensate()
            except Exception as compensation_error:
                log_error(f"Compensation failed: {str(compensation_error)}")
        
        # Re-throw the original error
        raise TransactionFailedError("Transaction failed and was rolled back") from e

Orchestrated Saga

Step 1 → Step 2 → Step 3 → ... → Step N → Success
   ↓        ↓        ↓              ↓
Comp 1 ← Comp 2 ← Comp 3 ← ... ← Comp N ← Failure

• Design idempotent compensation actions
• Implement comprehensive transaction logging
• Create clear visualization of transaction state
• Establish monitoring for stuck transactions
• Design for partial compensation scenarios

• Distributed transactions
• Microservice orchestration
• Cross-system data updates
• Long-running business processes
• Financial transaction processing

While each pattern addresses specific challenges, real-world ETL/ELT implementations typically combine multiple patterns to create comprehensive, resilient data pipelines. Here are examples of how these patterns can work together:

A real-time customer data integration pipeline might use:

• Streaming Architecture for low-latency data movement
• Log-Based CDC for efficient change detection
• Data Cleansing Pattern for customer data standardization
• Dead Letter Queue Pattern for handling problematic records
• Lineage Tracking Pattern for regulatory compliance

A financial data warehouse loading process could combine:

• Batch Processing Architecture for consistent, scheduled loads
• Validation-First Pattern for ensuring data accuracy
• Technical Metadata Capture for comprehensive documentation
• Fail-Fast Pattern for preventing partial or incorrect loads
• Compensating Transaction Pattern for maintaining consistency

An IoT data processing system might leverage:

• Micro-Batch Architecture for near real-time processing
• Query-Based CDC for identifying changed device data
• Quality Monitoring Pattern for tracking data quality trends
• Retry Pattern for handling connectivity issues
• Circuit Breaker Pattern for managing unstable device connections

When designing ETL/ELT systems, consider this decision framework to select the most appropriate patterns:

1. Data Movement Requirements
   • What is the required data latency?
   • What are the volume characteristics?
   • Are there specific scheduling requirements?
   • Is ordering or exactly-once processing required?
2. Change Detection Needs
   • What access do you have to source systems?
   • How frequent are the changes?
   • Is full historical tracking required?
   • What is the impact on source systems?
3. Data Quality Considerations
   • How critical is data accuracy?
   • What are the cleansing requirements?
   • Are there specific validation rules?
   • What quality monitoring is needed?
4. Metadata Requirements
   • What lineage tracking is required?
   • Is regulatory compliance a factor?
   • How important is self-documentation?
   • What operational monitoring is needed?
5. Error Handling Strategies
   • What is the impact of pipeline failure?
   • Are transient errors common?
   • How should invalid data be handled?
   • What recovery mechanisms are needed?

The field of data integration continues to evolve, with several emerging trends influencing ETL/ELT design patterns:

Moving from imperative to declarative pipeline definitions:

• Configuration-driven pipeline specifications
• Minimal custom code requirements
• Separation of what from how
• Improved maintainability and governance

Applying DevOps principles to data engineering:

• Pipeline-as-code implementations
• Automated testing for data pipelines
• CI/CD for data transformation logic
• Observability and monitoring integration

Leveraging cloud-native serverless architectures:

• Event-triggered pipeline execution
• Automatic scaling based on workload
• Consumption-based pricing models
• Reduced operational management

Moving toward unified processing models:

• Single codebase for batch and streaming
• Temporal query abstractions
• Stream and table duality concepts
• Consistent processing semantics

Shifting toward domain-oriented, distributed ownership:

• Domain-owned data products
• Self-service integration platforms
• Federated governance models
• Product thinking for data pipelines

ETL/ELT design patterns provide proven solutions to common data integration challenges, enabling engineers to build robust, scalable, and maintainable data pipelines. By understanding and applying these patterns appropriately, organizations can create data integration architectures that:

• Efficiently move data between systems
• Accurately detect and process changes
• Ensure consistent data quality
• Provide comprehensive documentation and lineage
• Handle errors gracefully

The key to success lies not in rigidly applying individual patterns, but in thoughtfully combining them based on specific business requirements, technical constraints, and organizational needs. As data volumes grow, sources diversify, and analytical demands become more sophisticated, these design patterns will continue to evolve—but their fundamental principles will remain invaluable guides for data engineers building the next generation of data integration systems.

---

Keywords: ETL design patterns, ELT architecture, data pipeline design, change data capture, CDC, data quality management, metadata management, data lineage, error handling, batch processing, stream processing, data integration, data engineering, data warehouse loading, ETL best practices

Hashtags: #ETLPatterns #ELTArchitecture #DataPipelines #DataEngineering #ChangeDataCapture #CDC #DataQuality #MetadataManagement #DataLineage #ErrorHandling #BatchProcessing #StreamProcessing #DataIntegration #DataWarehouse #ETLBestPractices