Streamlining Database Integrity Checks: Row Mismatch Detection and Alerts with Apache Airflow.

The system efficiently compares the row counts of the two databases and identifies any discrepancies. In the event of a mismatch, it triggers an automated email notification to designated stakeholders, ensuring timely awareness and intervention.

This solution was developed to streamline database integrity verification processes and significantly reduce manual monitoring. By automating these tasks with Airflow, it enhances the efficiency, accuracy, and reliability of data synchronization between systems, mitigating the risk of errors due to data inconsistencies.

Key Features:

• Automated Row Count Comparison: The script runs periodic checks (via Airflow DAGs) to compare the row counts between source and destination databases.
• Mismatch Detection: If discrepancies are found in the row count, the system promptly detects the mismatch and triggers appropriate actions.
• Real-time Email Alerts: Airflow’s Email Operator automatically sends email notifications to designated recipients whenever a mismatch is identified.
• Improved Data Integrity: Ensures data remains synchronized across databases, reducing the likelihood of data inconsistency issues.
• Efficient Monitoring: Airflow schedules and executes tasks, eliminating the need for manual comparisons and reducing human error, thereby improving operational Efficiency.

Technical Implementation:

• Python Scripting Developed the core automation logic in Python that connects to both source and destination databases, compares row counts, and sends email alerts when mismatches are detected.

• Apache Airflow Integration:

DAGs: Utilized Apache Airflow’s DAGs to orchestrate tasks such as connecting to databases, comparing row counts, and sending email notifications.

Task Scheduling: Leveraged Airflow’s built-in scheduling features to run checks at predefined intervals (e.g., daily, weekly).

Task Dependencies: Defined task dependencies in Airflow to ensure that tasks execute in the correct sequence and error handling is managed properly.

• Database Connectivity: Integrated with relational databases (e.g., MySQL, PostgreSQL)using Python’s database connectors (e.g., MySQL-connector, psycopg2).
• Email Automation: Used Python’s smtplib and Airflow’s Email Operator for automated email notifications.
• Error Handling: Implemented robust error handling mechanisms in both the Python script and Airflow tasks to manage database connection issues and email delivery failures.

Outcome:

• Reduced Manual Effort: Automation of the row count comparison and alerting process reduced manual intervention significantly.
• Increased Operational Efficiency: By using Airflow, the system’s checks are automated and scheduled, leading to improved efficiency.
• Enhanced Data Integrity: Ensured consistent and synchronized data across multiple databases with automated alerts for any discrepancies.
• Scalability: The use of Airflow makes the system highly scalable, allowing for easy adjustments to the workflow as needed.

This automation, powered by Airflow, has proven to be an essential tool for maintaining high-quality, error-free data synchronization across systems and ensuring timely interventions in case of discrepancies.

Skills & Technologies Used: Python, Apache Airflow, MySQL, PostgreSQL, Database Connectivity, Automation Scripting, Email Automation (smtplib, Email Operator), Data Integrity, Error Handling.