SHARE Pittsburgh’s conference program is centered around the theme of actionable intelligence for mainframe ecosystems. Technical sessions are focused on a key set of learning outcomes, tied to industry trends and member interests.
Interested in presenting a session? The call for proposals is open through April 14.
Learning Outcome
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What It Covers
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Modernize Data Management and Analytics
Optimize data storage, access, and analysis capabilities |
- Implement efficient data archiving and retrieval
- Set up real-time data replication and synchronization
- Enable modern analytics on mainframe data
- Optimize database performance and storage
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Integrate with Distributed Systems and Cloud
Connect mainframe systems with modern distributed architectures |
- Establish secure connectivity between mainframe and cloud
- Implement hybrid application architectures
- Enable real-time data exchange and synchronization
- Create unified monitoring across platforms
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Ensure High Availability and Disaster Recovery
Implement robust backup, recovery, and failover capabilities |
- Achieve 99.99%+ system availability
- Reduce recovery time objectives (RTO) and recovery point objectives (RPO)
- Implement automated failover and recovery procedures
- Ensure data consistency across recovery scenarios
- Data resiliency specific to application owner
- System reliability in the data centers
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Troubleshoot and Resolve Complex Issues
Develop advanced problem-solving and diagnostic skills |
- Reduce mean time to resolution (MTTR) for critical issues
- Improve first-call resolution rates
- Develop expertise in complex system diagnostics
- Implement proactive problem prevention
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Optimize Storage and Data Movement
Implement efficient storage management and data lifecycle policies |
- Reduce storage costs and improve utilization
- Implement automated data lifecycle management
- Optimize backup and archive strategies
- Improve data access performance
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Manage Workloads and Capacity Planning
Optimize resource allocation and plan for future growth |
- Implement effective workload balancing
- Accurate capacity forecasting and planning
- Optimize LPAR and resource allocation
- Manage peak workload scenarios
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