Data Capture

Enhanced Definition

Key Characteristics

- Log-Based Capture: The most common and efficient method on z/OS, where changes are captured directly from database transaction logs (e.g., DB2 logs, IMS logs) or file system journals, minimizing impact on source systems.
- Real-time or Near Real-time: Data capture solutions often operate continuously, providing updates with minimal latency, crucial for keeping target systems synchronized or for immediate analytics.
- Granular Change Detection: Captures specific inserts, updates, and deletes at the row or record level, including the before and after images of changed data.
- Source System Agnostic: While primarily focused on z/OS data sources (DB2, IMS, VSAM), captured data can be replicated to various targets, both on and off the mainframe.
- Low Overhead: Efficient data capture mechanisms are designed to impose minimal performance overhead on the critical production systems from which data is being captured.

Use Cases

- Database Replication: Synchronizing data between a primary z/OS database and a secondary database (either another z/OS system or a distributed platform) for high availability, disaster recovery, or workload balancing.
- Data Warehousing and ETL: Feeding operational data changes from OLTP systems into data warehouses or data marts for analytical processing, reducing the need for full batch extracts.
- Auditing and Compliance: Maintaining a detailed historical record of all data modifications for regulatory compliance, security audits, and forensic analysis.
- Real-time Analytics: Providing up-to-the-minute operational data to analytical platforms for immediate business intelligence and decision-making.
- Data Migration and Synchronization: Facilitating the movement of data between different systems or keeping disparate systems consistent over time.

Related Concepts

Data Capture is intrinsically linked to data replication, ETL (Extract, Transform, Load) processes, and high availability/disaster recovery (HA/DR) strategies. It heavily relies on the integrity of transaction logs (e.g., DB2's Write-Ahead Log, IMS logs) to reliably identify and extract changes. It serves as a foundational component for data warehousing by providing a continuous stream of updated operational data, and it contributes to data governance by enabling comprehensive auditing of data modifications.

Best Practices:

Monitor Performance: Regularly monitor the performance of the data capture process and its impact on the source system to ensure it doesn't degrade critical OLTP workloads.
Ensure Data Integrity: Implement robust error handling and recovery mechanisms to guarantee that all changes are captured and applied accurately and in the correct order, maintaining transactional consistency.
Optimize Log Reading: For log-based capture, configure the solution to efficiently read and process logs, potentially leveraging dedicated log readers or offloading log processing where possible.
Secure Data in Transit: Encrypt captured data during transmission to target systems, especially when moving data off the mainframe, to protect sensitive information.
Plan for Volume: Design the data capture infrastructure to handle expected data change volumes and peak loads, ensuring sufficient processing power and network bandwidth.