Data Pipeline Integration with TuskLang

🚀 Revolutionary Data Processing - Where Configuration Meets Intelligence

TuskLang transforms data pipelines from rigid, hardcoded processes into intelligent, adaptive systems that respond to your data patterns and business needs. No more fighting with complex ETL tools - TuskLang brings the power of intelligent data processing to your fingertips.

"We don't bow to any king" - especially not to bloated data pipeline frameworks that require armies of engineers to maintain.

🎯 Core Data Pipeline Capabilities

Intelligent ETL Processing

#!/bin/bash
TuskLang-powered ETL pipeline
source tusk.sh
Dynamic data extraction with intelligent scheduling
extract_config="
[data_extraction]
source: @env('DATA_SOURCE', 'postgresql://localhost/analytics')
schedule: @learn('optimal_extraction_time', '0 2   *')
batch_size: @optimize('batch_size', 1000)
parallel_workers: @metrics('cpu_cores', 4)
[transformation]
rules: @file.read('transformation_rules.tsk')
validation: @validate.schema('data_schema.json')
quality_checks: @cache('1h', 'quality_thresholds')
[loading]
target: @env('DATA_WAREHOUSE', 's3://analytics-bucket')
compression: @learn('optimal_compression', 'gzip')
partitioning: @optimize('partition_strategy', 'date')
"
Execute intelligent ETL pipeline
tsk process --config <(echo "$extract_config") --pipeline etl

Real-Time Streaming Integration

#!/bin/bash
Real-time data streaming with TuskLang
stream_config="
[stream_processor]
input_topic: @env('KAFKA_TOPIC', 'user_events')
output_topic: @env('PROCESSED_EVENTS', 'processed_events')
window_size: @learn('optimal_window', '5m')
parallelism: @metrics('available_cores', 8)
[stream_rules]
filter: @file.read('stream_filters.tsk')
aggregation: @cache('30s', 'aggregation_rules')
alerting: @validate.threshold('error_rate', 0.01)
[stream_monitoring]
metrics: @metrics.collect('stream_throughput')
health_check: @health.stream('kafka_connectivity')
backpressure: @monitor.backpressure('input_queue')
"
Start intelligent streaming pipeline
tsk stream --config <(echo "$stream_config") --real-time

🔄 ETL Process Orchestration

Multi-Stage Data Processing

#!/bin/bash
Complex ETL orchestration with TuskLang
etl_pipeline="
[pipeline_stages]
stage1_extract:
  type: extract
  source: @env('SOURCE_DB')
  query: @file.read('extract_queries.sql')
  parallel: @optimize('extract_parallelism', 4)
stage2_transform:
  type: transform
  rules: @file.read('transform_rules.tsk')
  validation: @validate.data_quality()
  cache: @cache('2h', 'transformed_data')
stage3_load:
  type: load
  target: @env('TARGET_WAREHOUSE')
  strategy: @learn('load_strategy', 'incremental')
  rollback: @backup.create('pre_load_snapshot')
[error_handling]
retry_attempts: @env('MAX_RETRIES', 3)
dead_letter_queue: @env('DLQ_TOPIC', 'failed_records')
notification: @alert.send('pipeline_failure')
"
Execute orchestrated ETL pipeline
tsk pipeline --config <(echo "$etl_pipeline") --monitor

Data Quality Assurance

#!/bin/bash
Comprehensive data quality checks
quality_config="
[data_quality]
completeness: @validate.completeness('required_fields')
accuracy: @validate.accuracy('business_rules')
consistency: @validate.consistency('cross_table_checks')
timeliness: @validate.timeliness('data_freshness')
[quality_thresholds]
min_completeness: @env('MIN_COMPLETENESS', 0.95)
max_duplicates: @env('MAX_DUPLICATES', 0.01)
data_freshness_hours: @env('MAX_AGE_HOURS', 24)
[quality_monitoring]
dashboard: @metrics.dashboard('data_quality')
alerts: @alert.threshold('quality_score', 0.9)
reports: @report.generate('quality_metrics')
"
Run data quality assessment
tsk quality --config <(echo "$quality_config") --assess

📊 Batch Processing Intelligence

Intelligent Batch Scheduling

#!/bin/bash
Smart batch processing with adaptive scheduling
batch_config="
[batch_processor]
input_path: @env('BATCH_INPUT_PATH')
output_path: @env('BATCH_OUTPUT_PATH')
batch_size: @learn('optimal_batch_size', 10000)
parallel_jobs: @metrics('available_memory_gb', 4)
[scheduling]
frequency: @learn('processing_frequency', 'hourly')
peak_hours: @optimize('peak_avoidance', 'true')
resource_aware: @metrics('system_load', 'adaptive')
[optimization]
compression: @learn('compression_ratio', 'gzip')
partitioning: @optimize('partition_size', '1gb')
caching: @cache('4h', 'processed_batches')
"
Execute intelligent batch processing
tsk batch --config <(echo "$batch_config") --optimize

Incremental Processing

#!/bin/bash
Incremental data processing with change detection
incremental_config="
[incremental_processing]
change_detection: @file.watch('source_directory')
last_processed: @cache('persistent', 'last_timestamp')
delta_strategy: @learn('delta_method', 'timestamp')
[processing_logic]
new_records: @query('SELECT * FROM source WHERE updated_at > @cache("last_processed")')
modified_records: @query('SELECT * FROM source WHERE modified_at > @cache("last_processed")')
deleted_records: @query('SELECT id FROM audit_log WHERE action = "DELETE" AND timestamp > @cache("last_processed")')
[consistency]
transactional: @env('TRANSACTIONAL_PROCESSING', 'true')
rollback_capability: @backup.create('pre_incremental')
verification: @validate.consistency('post_processing')
"
Run incremental processing
tsk incremental --config <(echo "$incremental_config") --detect-changes

🔄 Streaming Data Processing

Real-Time Event Processing

#!/bin/bash
Real-time event stream processing
stream_config="
[event_stream]
input_stream: @env('EVENT_STREAM_URL')
output_stream: @env('PROCESSED_STREAM_URL')
processing_latency: @metrics('target_latency_ms', 100)
[event_processing]
filtering: @file.read('event_filters.tsk')
enrichment: @cache('5m', 'enrichment_data')
aggregation: @learn('aggregation_window', '1m')
[stream_monitoring]
throughput: @metrics.collect('events_per_second')
error_rate: @metrics.collect('processing_errors')
backpressure: @monitor.queue('input_buffer')
"
Start real-time event processing
tsk stream --config <(echo "$stream_config") --events

Complex Event Processing (CEP)

#!/bin/bash
Complex event pattern detection
cep_config="
[pattern_detection]
patterns: @file.read('event_patterns.tsk')
correlation_window: @learn('correlation_time', '5m')
confidence_threshold: @env('CONFIDENCE_THRESHOLD', 0.8)
[event_correlation]
temporal: @validate.temporal('event_sequence')
spatial: @validate.spatial('event_location')
causal: @validate.causal('event_dependencies')
[alerting]
pattern_matched: @alert.send('pattern_detected')
anomaly_detected: @alert.send('anomaly_alert')
trend_identified: @alert.send('trend_notification')
"
Start complex event processing
tsk cep --config <(echo "$cep_config") --detect-patterns

📈 Analytics and Reporting

Real-Time Analytics Dashboard

#!/bin/bash
Real-time analytics with TuskLang
analytics_config="
[real_time_analytics]
data_sources: @env('ANALYTICS_SOURCES')
computation_interval: @learn('computation_frequency', '30s')
dashboard_refresh: @env('DASHBOARD_REFRESH', '5s')
[metrics_computation]
kpis: @file.read('kpi_definitions.tsk')
calculations: @cache('1m', 'metric_formulas')
trends: @learn('trend_detection', 'moving_average')
[visualization]
charts: @file.read('chart_configs.tsk')
alerts: @alert.threshold('kpi_thresholds')
exports: @report.schedule('hourly_reports')
"
Start real-time analytics
tsk analytics --config <(echo "$analytics_config") --dashboard

Predictive Analytics

#!/bin/bash
Predictive analytics with machine learning
predictive_config="
[machine_learning]
models: @file.read('ml_models.tsk')
training_data: @env('TRAINING_DATA_PATH')
prediction_interval: @learn('prediction_frequency', '1h')
[model_management]
versioning: @version.control('model_versions')
performance: @metrics.model('accuracy_score')
retraining: @learn('retraining_schedule', 'weekly')
[predictions]
forecasting: @ml.predict('demand_forecast')
anomaly_detection: @ml.detect('anomalies')
recommendations: @ml.recommend('user_preferences')
"
Start predictive analytics
tsk ml --config <(echo "$predictive_config") --predict

🔧 Advanced Pipeline Features

Data Lineage and Governance

#!/bin/bash
Data lineage tracking and governance
lineage_config="
[data_lineage]
tracking: @env('LINEAGE_TRACKING', 'true')
metadata: @file.read('metadata_schema.tsk')
audit_trail: @audit.track('data_movements')
[governance]
policies: @file.read('data_policies.tsk')
compliance: @validate.compliance('gdpr_requirements')
retention: @policy.retention('data_lifecycle')
[discovery]
catalog: @catalog.update('data_assets')
search: @search.index('metadata')
documentation: @docs.generate('data_dictionary')
"
Enable data governance
tsk governance --config <(echo "$lineage_config") --track-lineage

Performance Optimization

#!/bin/bash
Pipeline performance optimization
performance_config="
[optimization]
resource_monitoring: @metrics.system('cpu_memory_disk')
bottleneck_detection: @monitor.bottlenecks('pipeline_stages')
auto_scaling: @scale.auto('processing_capacity')
[caching_strategy]
hot_data: @cache('1h', 'frequently_accessed')
warm_data: @cache('4h', 'moderately_accessed')
cold_data: @storage.archive('rarely_accessed')
[parallelization]
data_parallel: @parallel.data('partition_processing')
task_parallel: @parallel.task('independent_operations')
pipeline_parallel: @parallel.pipeline('stage_overlap')
"
Optimize pipeline performance
tsk optimize --config <(echo "$performance_config") --auto-tune

🛠️ Troubleshooting and Monitoring

Pipeline Health Monitoring

#!/bin/bash
Comprehensive pipeline monitoring
monitoring_config="
[health_checks]
connectivity: @health.check('data_source_connectivity')
performance: @health.check('processing_performance')
quality: @health.check('data_quality_metrics')
[alerting]
critical_alerts: @alert.critical('pipeline_failure')
warning_alerts: @alert.warning('performance_degradation')
info_alerts: @alert.info('pipeline_status')
[logging]
structured_logs: @log.structured('pipeline_events')
error_tracking: @log.errors('processing_errors')
audit_logs: @log.audit('data_access')
"
Monitor pipeline health
tsk monitor --config <(echo "$monitoring_config") --health-check

Debugging and Diagnostics

#!/bin/bash
Advanced debugging capabilities
debug_config="
[debugging]
step_by_step: @debug.enable('step_execution')
data_inspection: @debug.inspect('intermediate_data')
performance_profiling: @debug.profile('execution_time')
[diagnostics]
error_analysis: @diagnose.errors('failure_patterns')
performance_analysis: @diagnose.performance('bottlenecks')
data_analysis: @diagnose.data('quality_issues')
[recovery]
auto_recovery: @recovery.auto('transient_failures')
manual_recovery: @recovery.manual('manual_intervention')
rollback_capability: @recovery.rollback('failed_changes')
"
Enable debugging mode
tsk debug --config <(echo "$debug_config") --diagnose

🔒 Security and Compliance

Data Security

#!/bin/bash
Data security and privacy protection
security_config="
[data_protection]
encryption: @encrypt.data('sensitive_fields')
masking: @mask.sensitive('pii_data')
tokenization: @tokenize.pii('credit_cards')
[access_control]
authentication: @auth.verify('user_credentials')
authorization: @auth.authorize('data_access')
audit_logging: @audit.access('data_operations')
[compliance]
gdpr_compliance: @compliance.gdpr('data_processing')
sox_compliance: @compliance.sox('financial_data')
hipaa_compliance: @compliance.hipaa('healthcare_data')
"
Apply security measures
tsk secure --config <(echo "$security_config") --protect-data

📚 Best Practices and Patterns

Pipeline Design Patterns

#!/bin/bash
Common pipeline design patterns
patterns_config="
[design_patterns]
fan_out_fan_in: @pattern.fan_out('parallel_processing')
filter_map_reduce: @pattern.fmap_reduce('data_transformation')
publish_subscribe: @pattern.pub_sub('event_distribution')
[error_handling]
circuit_breaker: @pattern.circuit_breaker('fault_tolerance')
retry_with_backoff: @pattern.retry('transient_failures')
dead_letter_queue: @pattern.dlq('failed_messages')
[scalability]
horizontal_scaling: @pattern.horizontal('load_distribution')
vertical_scaling: @pattern.vertical('resource_increase')
auto_scaling: @pattern.auto_scale('demand_based')
"
Apply design patterns
tsk patterns --config <(echo "$patterns_config") --apply

🚀 Getting Started with Data Pipelines

Quick Start Example

#!/bin/bash
Simple data pipeline example
simple_pipeline="
[simple_etl]
extract:
  source: 'postgresql://localhost/sales'
  query: 'SELECT * FROM orders WHERE created_at > @date.yesterday()'
  
transform:
  rules: |
    - filter: 'amount > 100'
    - aggregate: 'sum(amount) by customer_id'
    - enrich: '@cache("customer_data")'
    
load:
  target: 's3://analytics/processed_orders'
  format: 'parquet'
  compression: 'snappy'
"
Run simple pipeline
tsk pipeline --config <(echo "$simple_pipeline") --execute

📖 Related Documentation

- Database Integration: 004-database-integration-bash.md - @ Operator System: 031-sql-operator-bash.md - Performance Optimization: 086-error-handling-bash.md - Monitoring Integration: 083-monitoring-integration-bash.md - Event-Driven Architecture: 094-event-driven-architecture-bash.md

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Ready to revolutionize your data pipelines with TuskLang's intelligent processing capabilities?