Data engineers spend 40-60% of their time on operational tasks—monitoring pipelines, validating schemas, tracking lineage, and responding to alerts. This toolkit automates the operational burden so you can focus on architecting resilient, scalable data systems.

Problem: Manually checking dozens of ETL jobs across different systems to ensure they completed successfully.

Solution: Centralized monitoring of all data pipelines with automated health checks and alerting.

Features:

• Tracks execution status across multiple pipelines
• Calculates success rates and performance metrics
• Detects consecutive failures and performance degradation
• Identifies overdue jobs based on expected schedules
• Generates consolidated health reports
• Sends alerts on critical issues

Usage:

from pipeline_monitor import PipelineHealthMonitor

monitor = PipelineHealthMonitor()

# Provide execution logs for your pipelines
logs = {
    'daily_sales_etl': sales_log_df,
    'hourly_inventory_sync': inventory_log_df
}

report = monitor.monitor_all_pipelines(logs)
print(report)

Problem: Upstream data sources change without warning, breaking pipelines downstream.

Solution: Automatic schema comparison and drift detection with baseline tracking.

Features:

• Extracts schemas from databases or DataFrames
• Compares against stored baseline schemas
• Detects added/removed columns
• Identifies data type changes
• Tracks nullable constraint changes
• Generates detailed change reports
• Prevents breaking changes from propagating

Usage:

from schema_validator import SchemaValidator

validator = SchemaValidator()

# Create baseline from current schema
current_schema = validator.extract_schema_from_db(engine, 'users')
validator.save_baseline(current_schema, 'users_baseline')

# Later, validate against baseline
new_schema = validator.extract_schema_from_db(engine, 'users')
report = validator.validate_and_report(new_schema, 'users_baseline')
print(report)

Problem: No visibility into data dependencies and impact of changes across the data infrastructure.

Solution: Automated lineage mapping by parsing SQL and ETL code with visual dependency graphs.

Features:

• Parses SQL queries to extract dependencies
• Builds directed graph of data flow
• Tracks transformations at each stage
• Performs upstream/downstream impact analysis
• Generates Mermaid diagrams for visualization
• Exports lineage to JSON
• Identifies circular dependencies

Usage:

from lineage_tracker import DataLineageTracker

tracker = DataLineageTracker()

# Add lineage relationships
tracker.add_lineage(
    ['raw_orders', 'raw_customers'], 
    'stg_orders',
    'Join orders with customer data'
)

# Perform impact analysis
print(tracker.impact_analysis('stg_orders'))

# Generate visual diagram
diagram = tracker.generate_mermaid_diagram('stg_orders')

Problem: Database performance issues are difficult to diagnose without manual investigation.

Solution: Automated performance analysis with actionable optimization recommendations.

Features:

• Identifies slow-running queries with execution statistics
• Detects missing indexes based on sequential scan ratios
• Finds table bloat and vacuum candidates
• Locates unused indexes wasting space
• Analyzes table and index sizes
• Monitors connection pool statistics
• Generates SQL commands to fix issues
• Supports PostgreSQL and MySQL

Usage:

from db_performance_analyzer import DatabasePerformanceAnalyzer
from sqlalchemy import create_engine

engine = create_engine('postgresql://user:password@localhost/db')
analyzer = DatabasePerformanceAnalyzer(engine)

report = analyzer.generate_performance_report()
print(report)

analyzer.export_recommendations('optimization_plan.json')

Problem: Data quality checks scattered across scripts with no consistent framework or reporting.

Solution: Declarative assertion framework with detailed failure reporting and pipeline integration.

Features:

• Declarative assertion syntax
• Built-in assertions (nulls, uniqueness, ranges, foreign keys, patterns)
• Custom assertion support
• Severity levels (error/warning/info)
• Detailed failure reports with context
• Failed row extraction
• JSON export of results
• Stop-on-error mode

Usage:

from data_quality_framework import DataQualityFramework

dq = DataQualityFramework('users_table')

# Add assertions
dq.assert_row_count_range(min_rows=1000, max_rows=1000000)
dq.assert_no_nulls(['user_id', 'email'])
dq.assert_unique('user_id')
dq.assert_value_range('age', min_value=0, max_value=120)
dq.assert_foreign_key('department_id', dept_df, 'dept_id')

# Run all checks
passed = dq.run_all_assertions(df)

# Generate report
print(dq.generate_report())

# Export results
dq.export_results('quality_report.json')

pip install pandas sqlalchemy schedule fuzzywuzzy psycopg2-binary pymysql numpy scipy

1. Identify your biggest operational pain point
2. Copy the relevant script to your workspace
3. Configure for your database/orchestration system
4. Integrate into your operational workflows
5. Customize thresholds and alerts for your needs

• Python 3.7+
• pandas >= 1.3.0
• sqlalchemy >= 1.4.0
• psycopg2-binary >= 2.9.0 (for PostgreSQL)
• pymysql >= 1.0.0 (for MySQL)
• fuzzywuzzy >= 0.18.0
• schedule >= 1.1.0
• numpy >= 1.21.0
• scipy >= 1.7.0

• apache-airflow (for DAG integration)
• slack-sdk (for Slack notifications)
• pyyaml (for YAML configuration)

Execution Logs → Health Checker → Status Report → Alert System
                       ↓
                 Metric Calculator
                       ↓
                Historical Tracking

Current Schema → Comparator → Change Detector → Report Generator
                      ↓
              Baseline Storage

SQL/ETL Code → Parser → Graph Builder → Impact Analyzer → Visualizer

Database Stats → Analyzer → Recommender → Action Generator
                    ↓
              Metric Collector

Assertions → Runner → Results Collector → Report Generator → Exporter
                ↓
         Failed Row Tracker