Mastering Oracle Exadata for the AI Era: Future-Proof Your Database Career
COURSE FORMAT & DELIVERY DETAILS Self-Paced, On-Demand Learning That Fits Your Schedule
This comprehensive learning experience is designed for professionals like you who demand flexibility without compromising depth. The course is entirely self-paced, with immediate online access granted upon enrollment. There are no fixed dates, deadlines, or time commitments, so you can progress at a speed that aligns with your personal and professional responsibilities. Designed for Rapid Implementation and Real-World Results
Learners consistently report seeing measurable improvements in their ability to manage, optimize, and scale Oracle Exadata environments within just two to three weeks of starting the program. Most complete the full curriculum in 6 to 8 weeks, dedicating 5 to 7 hours per week. However, because the course is modular and structured around practical outcomes, you can begin applying key strategies from Day One-long before completion. Lifetime Access, Continuous Updates, Always Relevant
Your enrollment includes lifetime access to all course materials. This means you’ll receive every future update automatically and at no additional cost, ensuring your knowledge remains cutting-edge as Oracle evolves its Exadata platform and AI-integrated database strategies. Whether new firmware releases, updated automation tools, or emerging AI-driven tuning methods are introduced, your access ensures you stay ahead. Accessible Anywhere, Anytime, on Any Device
The entire learning system is mobile-friendly and optimized for 24/7 global access. You can switch seamlessly between desktop, tablet, and smartphone, making it easy to study during commutes, between meetings, or from remote locations. Progress tracking ensures your learning state is preserved across devices. Direct Instructor Guidance and Expert Support
Throughout your journey, you are not alone. You will have direct access to Oracle Exadata specialists with over 20 years of enterprise deployment experience. These experts provide hands-on guidance, answer your technical queries, and help you troubleshoot real-world scenarios. Support is delivered through structured feedback channels and curated response frameworks, ensuring clarity and precision without delay. Certificate of Completion Issued by The Art of Service
Upon finishing the course, you’ll earn a Certificate of Completion issued by The Art of Service. This credential is globally recognized and respected by IT leaders, hiring managers, and Oracle partners. It validates your mastery of Exadata in the context of modern AI-driven workloads and demonstrates a commitment to professional excellence in high-performance database engineering. Transparent, One-Time Pricing – No Hidden Fees Ever
The total cost of this course is straightforward and clearly presented. There are no recurring charges, no surprise fees, and no upsells. What you see is exactly what you get-a single, all-inclusive investment in your career advancement. Accepted Payment Methods
- Visa
- Mastercard
- PayPal
100% Satisfied or Refunded - Zero Risk Guarantee
We stand firmly behind the value of this program. If you’re not completely satisfied with your experience, simply request a full refund within 30 days of enrollment. No questions asked. This guarantee eliminates your financial risk and reflects our confidence in the course’s transformative impact. What to Expect After Enrollment
After registering, you will receive a confirmation email acknowledging your enrollment. Shortly afterward, a separate email containing your secure access details will be sent once the course materials are prepared for delivery. This process ensures that all learners receive a polished, fully tested learning experience. “Will This Work for Me?” - Our Guarantee
If you’re a database administrator, cloud architect, systems engineer, or IT leader responsible for mission-critical Oracle environments, this course is engineered precisely for your success. It works even if you’ve only worked with virtualized Oracle instances, even if you’re new to Exadata hardware, and even if you’re transitioning from legacy systems into cloud-integrated architectures. Real professionals are already achieving results: - A lead DBA from a Fortune 500 financial institution reduced Exadata query latency by 68% using optimization techniques learned in Module 5.
- A cloud migration specialist in Germany used the automated workload isolation strategies from Module 12 to streamline a multimillion-dollar client transition to Exadata Cloud@Customer.
- A database architect in Singapore earned a 23% salary increase after showcasing her Certificate of Completion and newly documented Exadata tuning projects during a performance review.
This program works because it’s built on proven methodologies, real enterprise patterns, and decades of hands-on experience-not theory. Every concept is tied to a specific, measurable outcome that aligns with actual job responsibilities in Oracle-centric organizations. Invest with Confidence - Risk Reversal Built In
By offering lifetime access, continuous updates, expert support, and a full refund guarantee, we’ve reversed the risk entirely. You gain everything-career momentum, technical mastery, and professional recognition-while risking nothing. Your only cost is time, and we’ve structured the course to maximize ROI at every step.
EXTENSIVE and DETAILED COURSE CURRICULUM
Module 1: Foundations of Oracle Exadata in the AI Era - Understanding the role of Exadata in modern AI and machine learning data pipelines
- Evolution of Oracle Exadata from hardware appliance to AI-optimized data platform
- Key architectural differentiators: Smart Storage, Hybrid Columnar Compression, and In-Memory acceleration
- How Exadata enables low-latency data ingestion for real-time AI inference
- Overview of Exadata X9M and subsequent generations: capabilities and strategic advantages
- Differences between Exadata Cloud Service and on-premises deployments
- The role of Exadata in hybrid and multi-cloud AI architectures
- Defining the Exadata stack: database, storage, network, and management layers
- Understanding the Oracle Intelligent Infrastructure stack integration
- Setting up your learning environment: access models and sandbox provisioning
- Introduction to autonomous database features running on Exadata
- Real-world case study: AI-driven financial fraud detection on Exadata
- Exadata licensing models and implications for AI workloads
- Planning for AI scalability: resource estimation and growth modeling
- Introduction to performance baselining and KPIs for AI-adjacent database systems
Module 2: Exadata Architecture Deep Dive - Hardware components: compute nodes, storage servers, InfiniBand networking
- Smart Scan technology: how it reduces I/O for analytical queries
- Offloading processing: SQL execution pushed to storage cells
- InfiniBand architecture and its impact on AI data throughput
- Data and redo network separation: principles and configuration
- High Availability architecture: RAC, ASM, and Fast Recovery Area integration
- Understanding Smart Flash Cache and its role in query acceleration
- Cell storage indexes and their impact on predicate filtering
- Database Smart Flash Cache: configuration and tuning
- How NVMe drives improve IOPs for AI batch processing
- Exadata Software Home architecture and patching workflows
- Role of the Management Server (OMS) in centralized control
- Understanding storage indexes: creation, maintenance, and visibility
- How Exadata handles large object (LOB) storage for AI model inputs
- Exadata hybrid columnar compression: algorithm selection and trade-offs
Module 3: Exadata Deployment Models and Sizing Strategies - Comparing rack configurations: quarter, half, full, and engineered systems
- Cloud@Customer vs Exadata Cloud Service: decision framework
- Sizing for AI workloads: memory, CPU, storage, and I/O considerations
- Using Oracle’s sizing tools: DBSAT, AWR, and ExaWatcher
- Workload characterization: OLTP, data warehouse, mixed, and AI/ML
- Modeling concurrent AI inference requests and database stress
- Storage allocation strategies: grid disks, celldisks, and redundancy
- Network configuration: subnets, bonding, and VLANs for Exadata
- Deployment checklist for on-premises Exadata installations
- Migration path considerations: from non-Engineered Systems to Exadata
- Capacity planning for long-term AI data growth
- Evaluating memory requirements for In-Memory Column Store on Exadata
- Understanding Resource Manager plans during peak AI query loads
- Planning for Exadata storage expansion and non-disruptive upgrades
- Security and compliance implications of physical vs cloud Exadata
Module 4: Installation and Configuration Fundamentals - Prerequisites for Exadata deployment: IP addressing, DNS, NTP
- Using the Exadata Deployment Assistant (EDA)
- Initial configuration of database servers and storage cells
- Validating InfiniBand connectivity and fabric redundancy
- Creating initial ASM disk groups with optimal redundancy
- Database creation best practices on Exadata
- Configuring Oracle Clusterware and ASM
- Enabling and configuring IORM (Intelligent Resource Management)
- Setting up Exadata-specific monitoring tools
- Initial security hardening: SSH, user access, firewalls
- Validating Smart Scan functionality post-install
- Baseline performance testing: creating synthetic workloads
- Setting up OEM for Exadata oversight
- Configuration validation using exachk and other Oracle tools
- Documenting the initial state for compliance and audit
Module 5: Monitoring and Performance Optimization - Introduction to Exadata performance metrics: IOPS, latency, throughput
- Interpreting cell metrics: celldisk, hard disk, flash cache
- Using Database Console and OEM for real-time monitoring
- ExaWatcher: data collection, visualization, and analysis
- AWR and ASH reports tailored for Exadata environments
- Diagnosing I/O bottlenecks specific to AI-heavy queries
- Monitoring Smart Scan effectiveness and offload efficiency
- Flash Cache hit ratios and tuning strategies
- Database In-Memory and Exadata synergy analysis
- Interpreting IORM metrics: resource allocation and database metrics
- Configuring and using Exadata-specific alert thresholds
- Tracking storage index effectiveness using V$ views
- Querying X$KCFIO for low-level I/O statistics
- Correlating Exadata metrics with application performance
- Creating custom dashboards for AI workload visibility
- Benchmarking performance after configuration changes
Module 6: SQL Optimization on Exadata - Understanding Oracle optimizer behavior on Exadata
- Ensuring SQL benefits from Smart Scan: query transformation checks
- Identifying full table scans eligible for offloading
- Using SQL Monitoring reports to track Exadata-specific operations
- Tuning joins and filters to maximize storage offload
- Partitioning strategies optimized for Exadata and AI workloads
- Index design considerations: when to use, when to avoid
- Using invisible indexes for safe performance testing
- Adaptive execution plans and Exadata response
- SQL Plan Management: baselines, evolution, and stability
- Handling parallel execution on Exadata: DOP and queuing
- Automatic Degree of Parallelism (Auto DOP) tuning
- Minimizing data movement in RAC environments
- Statistics gathering strategies for large Exadata tables
- Handling skewed data distributions in AI training sets
- Using result caching for repeated AI query patterns
Module 7: Exadata Storage Management - Understanding celldisks, griddisks, and ASM allocation
- Creating and managing ASM disk groups for optimal performance
- Normal vs high redundancy: trade-offs for AI systems
- Mirroring policies in a two-or-more storage server environment
- Provisioning storage for database, recovery, and software areas
- Extending storage capacity: adding storage servers
- Rebalancing ASM disk groups after changes
- Monitoring free space and predicting exhaustion
- Handling cell failures and automatic recovery
- Using FLASHCACHE and FLASHLOG configurations
- Tuning storage indexes for specific query patterns
- Managing Hybrid Columnar Compression at table and partition levels
- Decompressing segments during updates or maintenance
- Moving databases between Exadata systems using backup and restore
- Monitoring cell smart table scan statistics
- Handling temporary and undo tablespaces on Exadata
Module 8: High Availability and Disaster Recovery - Oracle RAC architecture and Exadata integration
- ASM and its role in redundancy and resilience
- Fast disk resync and proactive failure detection
- Handling cell failures: impact and recovery procedures
- Database Flashback and Exadata: configuration and use cases
- Backup strategies: RMAN and Exadata Smart Flash Logging
- Configuring RMAN for optimal Exadata throughput
- Using Incremental Merge Backups to reduce backup windows
- Data Guard integration with Exadata databases
- Active Data Guard and real-time query offload to standby
- Far Sync instances in Exadata environments
- Snapshot Standby: use cases for AI testing and development
- Zero Data Loss Recovery Appliance with Exadata
- Backup compression and encryption on Exadata
- Testing and documenting DR failover procedures
- Validating RPO and RTO under AI workload stress
Module 9: Security and Compliance in Exadata - Database Vault configuration on Exadata
- Data Redaction for sensitive fields in AI datasets
- Transparent Data Encryption (TDE) at column, table, and tablespace levels
- Managing TDE wallets in an Exadata environment
- Secure backup practices with encrypted RMAN
- Auditing strategies: fine-grained and unified auditing
- Implementing row-level security for AI access control
- Monitoring privileged user activity on Exadata
- Patching strategies for security: downtime vs rolling patches
- Applying Exadata system patches using dbaascli and other tools
- Validating security posture with Oracle Configuration Manager
- Compliance frameworks: HIPAA, GDPR, PCI DSS on Exadata
- Data masking for non-production Exadata clones
- Managing multi-tenancy security in Exadata CDBs
- Firewall configuration and secure access patterns
Module 10: Automation and Intelligent Operations - Introduction to Oracle Autonomous Database on Exadata
- Automated patching, updates, and maintenance windows
- Automatic SQL tuning and index recommendations
- Using Machine Learning for performance anomaly detection
- Autonomous Health Framework (AHF) for diagnostics
- Exadata-specific health checks using Exachk
- Automated space management and segment advisor
- Intelligent Infrastructure alerts and recommended actions
- Auto Scaling in Exadata Cloud Service for AI spikes
- Automated backup and retention policies
- Scheduled maintenance insights and risk assessment
- Autonomous Database limitations and Exadata advantages
- Handling mixed workloads: autonomous vs manual tuning
- Automated statistics gathering on large Exadata tables
- Using Oracle Support tools with automated diagnostics upload
Module 11: Integration with AI and Analytics Platforms - Connecting Exadata to Python-based AI training pipelines
- Optimizing JDBC and ODP.NET connections for AI applications
- Using Oracle Machine Learning (OML) within Exadata
- Deploying R and Python scripts inside the database
- OML4SQL: built-in algorithms for in-database AI
- Feature engineering using SQL with large Exadata tables
- Model persistence and versioning in Exadata databases
- Secure data export for external AI training
- Performance of JSON and XML queries on Exadata for AI data
- Using In-Memory Column Store for real-time AI scoring
- Batch scoring at scale using Exadata parallelism
- Integrating with TensorFlow and PyTorch via Oracle Data Miner
- Data sampling strategies for model training from Exadata
- Latency benchmarks for inference query responses
- Securing AI model data access with VPD and TDE
Module 12: Workload Isolation and Resource Management - Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
Module 1: Foundations of Oracle Exadata in the AI Era - Understanding the role of Exadata in modern AI and machine learning data pipelines
- Evolution of Oracle Exadata from hardware appliance to AI-optimized data platform
- Key architectural differentiators: Smart Storage, Hybrid Columnar Compression, and In-Memory acceleration
- How Exadata enables low-latency data ingestion for real-time AI inference
- Overview of Exadata X9M and subsequent generations: capabilities and strategic advantages
- Differences between Exadata Cloud Service and on-premises deployments
- The role of Exadata in hybrid and multi-cloud AI architectures
- Defining the Exadata stack: database, storage, network, and management layers
- Understanding the Oracle Intelligent Infrastructure stack integration
- Setting up your learning environment: access models and sandbox provisioning
- Introduction to autonomous database features running on Exadata
- Real-world case study: AI-driven financial fraud detection on Exadata
- Exadata licensing models and implications for AI workloads
- Planning for AI scalability: resource estimation and growth modeling
- Introduction to performance baselining and KPIs for AI-adjacent database systems
Module 2: Exadata Architecture Deep Dive - Hardware components: compute nodes, storage servers, InfiniBand networking
- Smart Scan technology: how it reduces I/O for analytical queries
- Offloading processing: SQL execution pushed to storage cells
- InfiniBand architecture and its impact on AI data throughput
- Data and redo network separation: principles and configuration
- High Availability architecture: RAC, ASM, and Fast Recovery Area integration
- Understanding Smart Flash Cache and its role in query acceleration
- Cell storage indexes and their impact on predicate filtering
- Database Smart Flash Cache: configuration and tuning
- How NVMe drives improve IOPs for AI batch processing
- Exadata Software Home architecture and patching workflows
- Role of the Management Server (OMS) in centralized control
- Understanding storage indexes: creation, maintenance, and visibility
- How Exadata handles large object (LOB) storage for AI model inputs
- Exadata hybrid columnar compression: algorithm selection and trade-offs
Module 3: Exadata Deployment Models and Sizing Strategies - Comparing rack configurations: quarter, half, full, and engineered systems
- Cloud@Customer vs Exadata Cloud Service: decision framework
- Sizing for AI workloads: memory, CPU, storage, and I/O considerations
- Using Oracle’s sizing tools: DBSAT, AWR, and ExaWatcher
- Workload characterization: OLTP, data warehouse, mixed, and AI/ML
- Modeling concurrent AI inference requests and database stress
- Storage allocation strategies: grid disks, celldisks, and redundancy
- Network configuration: subnets, bonding, and VLANs for Exadata
- Deployment checklist for on-premises Exadata installations
- Migration path considerations: from non-Engineered Systems to Exadata
- Capacity planning for long-term AI data growth
- Evaluating memory requirements for In-Memory Column Store on Exadata
- Understanding Resource Manager plans during peak AI query loads
- Planning for Exadata storage expansion and non-disruptive upgrades
- Security and compliance implications of physical vs cloud Exadata
Module 4: Installation and Configuration Fundamentals - Prerequisites for Exadata deployment: IP addressing, DNS, NTP
- Using the Exadata Deployment Assistant (EDA)
- Initial configuration of database servers and storage cells
- Validating InfiniBand connectivity and fabric redundancy
- Creating initial ASM disk groups with optimal redundancy
- Database creation best practices on Exadata
- Configuring Oracle Clusterware and ASM
- Enabling and configuring IORM (Intelligent Resource Management)
- Setting up Exadata-specific monitoring tools
- Initial security hardening: SSH, user access, firewalls
- Validating Smart Scan functionality post-install
- Baseline performance testing: creating synthetic workloads
- Setting up OEM for Exadata oversight
- Configuration validation using exachk and other Oracle tools
- Documenting the initial state for compliance and audit
Module 5: Monitoring and Performance Optimization - Introduction to Exadata performance metrics: IOPS, latency, throughput
- Interpreting cell metrics: celldisk, hard disk, flash cache
- Using Database Console and OEM for real-time monitoring
- ExaWatcher: data collection, visualization, and analysis
- AWR and ASH reports tailored for Exadata environments
- Diagnosing I/O bottlenecks specific to AI-heavy queries
- Monitoring Smart Scan effectiveness and offload efficiency
- Flash Cache hit ratios and tuning strategies
- Database In-Memory and Exadata synergy analysis
- Interpreting IORM metrics: resource allocation and database metrics
- Configuring and using Exadata-specific alert thresholds
- Tracking storage index effectiveness using V$ views
- Querying X$KCFIO for low-level I/O statistics
- Correlating Exadata metrics with application performance
- Creating custom dashboards for AI workload visibility
- Benchmarking performance after configuration changes
Module 6: SQL Optimization on Exadata - Understanding Oracle optimizer behavior on Exadata
- Ensuring SQL benefits from Smart Scan: query transformation checks
- Identifying full table scans eligible for offloading
- Using SQL Monitoring reports to track Exadata-specific operations
- Tuning joins and filters to maximize storage offload
- Partitioning strategies optimized for Exadata and AI workloads
- Index design considerations: when to use, when to avoid
- Using invisible indexes for safe performance testing
- Adaptive execution plans and Exadata response
- SQL Plan Management: baselines, evolution, and stability
- Handling parallel execution on Exadata: DOP and queuing
- Automatic Degree of Parallelism (Auto DOP) tuning
- Minimizing data movement in RAC environments
- Statistics gathering strategies for large Exadata tables
- Handling skewed data distributions in AI training sets
- Using result caching for repeated AI query patterns
Module 7: Exadata Storage Management - Understanding celldisks, griddisks, and ASM allocation
- Creating and managing ASM disk groups for optimal performance
- Normal vs high redundancy: trade-offs for AI systems
- Mirroring policies in a two-or-more storage server environment
- Provisioning storage for database, recovery, and software areas
- Extending storage capacity: adding storage servers
- Rebalancing ASM disk groups after changes
- Monitoring free space and predicting exhaustion
- Handling cell failures and automatic recovery
- Using FLASHCACHE and FLASHLOG configurations
- Tuning storage indexes for specific query patterns
- Managing Hybrid Columnar Compression at table and partition levels
- Decompressing segments during updates or maintenance
- Moving databases between Exadata systems using backup and restore
- Monitoring cell smart table scan statistics
- Handling temporary and undo tablespaces on Exadata
Module 8: High Availability and Disaster Recovery - Oracle RAC architecture and Exadata integration
- ASM and its role in redundancy and resilience
- Fast disk resync and proactive failure detection
- Handling cell failures: impact and recovery procedures
- Database Flashback and Exadata: configuration and use cases
- Backup strategies: RMAN and Exadata Smart Flash Logging
- Configuring RMAN for optimal Exadata throughput
- Using Incremental Merge Backups to reduce backup windows
- Data Guard integration with Exadata databases
- Active Data Guard and real-time query offload to standby
- Far Sync instances in Exadata environments
- Snapshot Standby: use cases for AI testing and development
- Zero Data Loss Recovery Appliance with Exadata
- Backup compression and encryption on Exadata
- Testing and documenting DR failover procedures
- Validating RPO and RTO under AI workload stress
Module 9: Security and Compliance in Exadata - Database Vault configuration on Exadata
- Data Redaction for sensitive fields in AI datasets
- Transparent Data Encryption (TDE) at column, table, and tablespace levels
- Managing TDE wallets in an Exadata environment
- Secure backup practices with encrypted RMAN
- Auditing strategies: fine-grained and unified auditing
- Implementing row-level security for AI access control
- Monitoring privileged user activity on Exadata
- Patching strategies for security: downtime vs rolling patches
- Applying Exadata system patches using dbaascli and other tools
- Validating security posture with Oracle Configuration Manager
- Compliance frameworks: HIPAA, GDPR, PCI DSS on Exadata
- Data masking for non-production Exadata clones
- Managing multi-tenancy security in Exadata CDBs
- Firewall configuration and secure access patterns
Module 10: Automation and Intelligent Operations - Introduction to Oracle Autonomous Database on Exadata
- Automated patching, updates, and maintenance windows
- Automatic SQL tuning and index recommendations
- Using Machine Learning for performance anomaly detection
- Autonomous Health Framework (AHF) for diagnostics
- Exadata-specific health checks using Exachk
- Automated space management and segment advisor
- Intelligent Infrastructure alerts and recommended actions
- Auto Scaling in Exadata Cloud Service for AI spikes
- Automated backup and retention policies
- Scheduled maintenance insights and risk assessment
- Autonomous Database limitations and Exadata advantages
- Handling mixed workloads: autonomous vs manual tuning
- Automated statistics gathering on large Exadata tables
- Using Oracle Support tools with automated diagnostics upload
Module 11: Integration with AI and Analytics Platforms - Connecting Exadata to Python-based AI training pipelines
- Optimizing JDBC and ODP.NET connections for AI applications
- Using Oracle Machine Learning (OML) within Exadata
- Deploying R and Python scripts inside the database
- OML4SQL: built-in algorithms for in-database AI
- Feature engineering using SQL with large Exadata tables
- Model persistence and versioning in Exadata databases
- Secure data export for external AI training
- Performance of JSON and XML queries on Exadata for AI data
- Using In-Memory Column Store for real-time AI scoring
- Batch scoring at scale using Exadata parallelism
- Integrating with TensorFlow and PyTorch via Oracle Data Miner
- Data sampling strategies for model training from Exadata
- Latency benchmarks for inference query responses
- Securing AI model data access with VPD and TDE
Module 12: Workload Isolation and Resource Management - Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Hardware components: compute nodes, storage servers, InfiniBand networking
- Smart Scan technology: how it reduces I/O for analytical queries
- Offloading processing: SQL execution pushed to storage cells
- InfiniBand architecture and its impact on AI data throughput
- Data and redo network separation: principles and configuration
- High Availability architecture: RAC, ASM, and Fast Recovery Area integration
- Understanding Smart Flash Cache and its role in query acceleration
- Cell storage indexes and their impact on predicate filtering
- Database Smart Flash Cache: configuration and tuning
- How NVMe drives improve IOPs for AI batch processing
- Exadata Software Home architecture and patching workflows
- Role of the Management Server (OMS) in centralized control
- Understanding storage indexes: creation, maintenance, and visibility
- How Exadata handles large object (LOB) storage for AI model inputs
- Exadata hybrid columnar compression: algorithm selection and trade-offs
Module 3: Exadata Deployment Models and Sizing Strategies - Comparing rack configurations: quarter, half, full, and engineered systems
- Cloud@Customer vs Exadata Cloud Service: decision framework
- Sizing for AI workloads: memory, CPU, storage, and I/O considerations
- Using Oracle’s sizing tools: DBSAT, AWR, and ExaWatcher
- Workload characterization: OLTP, data warehouse, mixed, and AI/ML
- Modeling concurrent AI inference requests and database stress
- Storage allocation strategies: grid disks, celldisks, and redundancy
- Network configuration: subnets, bonding, and VLANs for Exadata
- Deployment checklist for on-premises Exadata installations
- Migration path considerations: from non-Engineered Systems to Exadata
- Capacity planning for long-term AI data growth
- Evaluating memory requirements for In-Memory Column Store on Exadata
- Understanding Resource Manager plans during peak AI query loads
- Planning for Exadata storage expansion and non-disruptive upgrades
- Security and compliance implications of physical vs cloud Exadata
Module 4: Installation and Configuration Fundamentals - Prerequisites for Exadata deployment: IP addressing, DNS, NTP
- Using the Exadata Deployment Assistant (EDA)
- Initial configuration of database servers and storage cells
- Validating InfiniBand connectivity and fabric redundancy
- Creating initial ASM disk groups with optimal redundancy
- Database creation best practices on Exadata
- Configuring Oracle Clusterware and ASM
- Enabling and configuring IORM (Intelligent Resource Management)
- Setting up Exadata-specific monitoring tools
- Initial security hardening: SSH, user access, firewalls
- Validating Smart Scan functionality post-install
- Baseline performance testing: creating synthetic workloads
- Setting up OEM for Exadata oversight
- Configuration validation using exachk and other Oracle tools
- Documenting the initial state for compliance and audit
Module 5: Monitoring and Performance Optimization - Introduction to Exadata performance metrics: IOPS, latency, throughput
- Interpreting cell metrics: celldisk, hard disk, flash cache
- Using Database Console and OEM for real-time monitoring
- ExaWatcher: data collection, visualization, and analysis
- AWR and ASH reports tailored for Exadata environments
- Diagnosing I/O bottlenecks specific to AI-heavy queries
- Monitoring Smart Scan effectiveness and offload efficiency
- Flash Cache hit ratios and tuning strategies
- Database In-Memory and Exadata synergy analysis
- Interpreting IORM metrics: resource allocation and database metrics
- Configuring and using Exadata-specific alert thresholds
- Tracking storage index effectiveness using V$ views
- Querying X$KCFIO for low-level I/O statistics
- Correlating Exadata metrics with application performance
- Creating custom dashboards for AI workload visibility
- Benchmarking performance after configuration changes
Module 6: SQL Optimization on Exadata - Understanding Oracle optimizer behavior on Exadata
- Ensuring SQL benefits from Smart Scan: query transformation checks
- Identifying full table scans eligible for offloading
- Using SQL Monitoring reports to track Exadata-specific operations
- Tuning joins and filters to maximize storage offload
- Partitioning strategies optimized for Exadata and AI workloads
- Index design considerations: when to use, when to avoid
- Using invisible indexes for safe performance testing
- Adaptive execution plans and Exadata response
- SQL Plan Management: baselines, evolution, and stability
- Handling parallel execution on Exadata: DOP and queuing
- Automatic Degree of Parallelism (Auto DOP) tuning
- Minimizing data movement in RAC environments
- Statistics gathering strategies for large Exadata tables
- Handling skewed data distributions in AI training sets
- Using result caching for repeated AI query patterns
Module 7: Exadata Storage Management - Understanding celldisks, griddisks, and ASM allocation
- Creating and managing ASM disk groups for optimal performance
- Normal vs high redundancy: trade-offs for AI systems
- Mirroring policies in a two-or-more storage server environment
- Provisioning storage for database, recovery, and software areas
- Extending storage capacity: adding storage servers
- Rebalancing ASM disk groups after changes
- Monitoring free space and predicting exhaustion
- Handling cell failures and automatic recovery
- Using FLASHCACHE and FLASHLOG configurations
- Tuning storage indexes for specific query patterns
- Managing Hybrid Columnar Compression at table and partition levels
- Decompressing segments during updates or maintenance
- Moving databases between Exadata systems using backup and restore
- Monitoring cell smart table scan statistics
- Handling temporary and undo tablespaces on Exadata
Module 8: High Availability and Disaster Recovery - Oracle RAC architecture and Exadata integration
- ASM and its role in redundancy and resilience
- Fast disk resync and proactive failure detection
- Handling cell failures: impact and recovery procedures
- Database Flashback and Exadata: configuration and use cases
- Backup strategies: RMAN and Exadata Smart Flash Logging
- Configuring RMAN for optimal Exadata throughput
- Using Incremental Merge Backups to reduce backup windows
- Data Guard integration with Exadata databases
- Active Data Guard and real-time query offload to standby
- Far Sync instances in Exadata environments
- Snapshot Standby: use cases for AI testing and development
- Zero Data Loss Recovery Appliance with Exadata
- Backup compression and encryption on Exadata
- Testing and documenting DR failover procedures
- Validating RPO and RTO under AI workload stress
Module 9: Security and Compliance in Exadata - Database Vault configuration on Exadata
- Data Redaction for sensitive fields in AI datasets
- Transparent Data Encryption (TDE) at column, table, and tablespace levels
- Managing TDE wallets in an Exadata environment
- Secure backup practices with encrypted RMAN
- Auditing strategies: fine-grained and unified auditing
- Implementing row-level security for AI access control
- Monitoring privileged user activity on Exadata
- Patching strategies for security: downtime vs rolling patches
- Applying Exadata system patches using dbaascli and other tools
- Validating security posture with Oracle Configuration Manager
- Compliance frameworks: HIPAA, GDPR, PCI DSS on Exadata
- Data masking for non-production Exadata clones
- Managing multi-tenancy security in Exadata CDBs
- Firewall configuration and secure access patterns
Module 10: Automation and Intelligent Operations - Introduction to Oracle Autonomous Database on Exadata
- Automated patching, updates, and maintenance windows
- Automatic SQL tuning and index recommendations
- Using Machine Learning for performance anomaly detection
- Autonomous Health Framework (AHF) for diagnostics
- Exadata-specific health checks using Exachk
- Automated space management and segment advisor
- Intelligent Infrastructure alerts and recommended actions
- Auto Scaling in Exadata Cloud Service for AI spikes
- Automated backup and retention policies
- Scheduled maintenance insights and risk assessment
- Autonomous Database limitations and Exadata advantages
- Handling mixed workloads: autonomous vs manual tuning
- Automated statistics gathering on large Exadata tables
- Using Oracle Support tools with automated diagnostics upload
Module 11: Integration with AI and Analytics Platforms - Connecting Exadata to Python-based AI training pipelines
- Optimizing JDBC and ODP.NET connections for AI applications
- Using Oracle Machine Learning (OML) within Exadata
- Deploying R and Python scripts inside the database
- OML4SQL: built-in algorithms for in-database AI
- Feature engineering using SQL with large Exadata tables
- Model persistence and versioning in Exadata databases
- Secure data export for external AI training
- Performance of JSON and XML queries on Exadata for AI data
- Using In-Memory Column Store for real-time AI scoring
- Batch scoring at scale using Exadata parallelism
- Integrating with TensorFlow and PyTorch via Oracle Data Miner
- Data sampling strategies for model training from Exadata
- Latency benchmarks for inference query responses
- Securing AI model data access with VPD and TDE
Module 12: Workload Isolation and Resource Management - Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Prerequisites for Exadata deployment: IP addressing, DNS, NTP
- Using the Exadata Deployment Assistant (EDA)
- Initial configuration of database servers and storage cells
- Validating InfiniBand connectivity and fabric redundancy
- Creating initial ASM disk groups with optimal redundancy
- Database creation best practices on Exadata
- Configuring Oracle Clusterware and ASM
- Enabling and configuring IORM (Intelligent Resource Management)
- Setting up Exadata-specific monitoring tools
- Initial security hardening: SSH, user access, firewalls
- Validating Smart Scan functionality post-install
- Baseline performance testing: creating synthetic workloads
- Setting up OEM for Exadata oversight
- Configuration validation using exachk and other Oracle tools
- Documenting the initial state for compliance and audit
Module 5: Monitoring and Performance Optimization - Introduction to Exadata performance metrics: IOPS, latency, throughput
- Interpreting cell metrics: celldisk, hard disk, flash cache
- Using Database Console and OEM for real-time monitoring
- ExaWatcher: data collection, visualization, and analysis
- AWR and ASH reports tailored for Exadata environments
- Diagnosing I/O bottlenecks specific to AI-heavy queries
- Monitoring Smart Scan effectiveness and offload efficiency
- Flash Cache hit ratios and tuning strategies
- Database In-Memory and Exadata synergy analysis
- Interpreting IORM metrics: resource allocation and database metrics
- Configuring and using Exadata-specific alert thresholds
- Tracking storage index effectiveness using V$ views
- Querying X$KCFIO for low-level I/O statistics
- Correlating Exadata metrics with application performance
- Creating custom dashboards for AI workload visibility
- Benchmarking performance after configuration changes
Module 6: SQL Optimization on Exadata - Understanding Oracle optimizer behavior on Exadata
- Ensuring SQL benefits from Smart Scan: query transformation checks
- Identifying full table scans eligible for offloading
- Using SQL Monitoring reports to track Exadata-specific operations
- Tuning joins and filters to maximize storage offload
- Partitioning strategies optimized for Exadata and AI workloads
- Index design considerations: when to use, when to avoid
- Using invisible indexes for safe performance testing
- Adaptive execution plans and Exadata response
- SQL Plan Management: baselines, evolution, and stability
- Handling parallel execution on Exadata: DOP and queuing
- Automatic Degree of Parallelism (Auto DOP) tuning
- Minimizing data movement in RAC environments
- Statistics gathering strategies for large Exadata tables
- Handling skewed data distributions in AI training sets
- Using result caching for repeated AI query patterns
Module 7: Exadata Storage Management - Understanding celldisks, griddisks, and ASM allocation
- Creating and managing ASM disk groups for optimal performance
- Normal vs high redundancy: trade-offs for AI systems
- Mirroring policies in a two-or-more storage server environment
- Provisioning storage for database, recovery, and software areas
- Extending storage capacity: adding storage servers
- Rebalancing ASM disk groups after changes
- Monitoring free space and predicting exhaustion
- Handling cell failures and automatic recovery
- Using FLASHCACHE and FLASHLOG configurations
- Tuning storage indexes for specific query patterns
- Managing Hybrid Columnar Compression at table and partition levels
- Decompressing segments during updates or maintenance
- Moving databases between Exadata systems using backup and restore
- Monitoring cell smart table scan statistics
- Handling temporary and undo tablespaces on Exadata
Module 8: High Availability and Disaster Recovery - Oracle RAC architecture and Exadata integration
- ASM and its role in redundancy and resilience
- Fast disk resync and proactive failure detection
- Handling cell failures: impact and recovery procedures
- Database Flashback and Exadata: configuration and use cases
- Backup strategies: RMAN and Exadata Smart Flash Logging
- Configuring RMAN for optimal Exadata throughput
- Using Incremental Merge Backups to reduce backup windows
- Data Guard integration with Exadata databases
- Active Data Guard and real-time query offload to standby
- Far Sync instances in Exadata environments
- Snapshot Standby: use cases for AI testing and development
- Zero Data Loss Recovery Appliance with Exadata
- Backup compression and encryption on Exadata
- Testing and documenting DR failover procedures
- Validating RPO and RTO under AI workload stress
Module 9: Security and Compliance in Exadata - Database Vault configuration on Exadata
- Data Redaction for sensitive fields in AI datasets
- Transparent Data Encryption (TDE) at column, table, and tablespace levels
- Managing TDE wallets in an Exadata environment
- Secure backup practices with encrypted RMAN
- Auditing strategies: fine-grained and unified auditing
- Implementing row-level security for AI access control
- Monitoring privileged user activity on Exadata
- Patching strategies for security: downtime vs rolling patches
- Applying Exadata system patches using dbaascli and other tools
- Validating security posture with Oracle Configuration Manager
- Compliance frameworks: HIPAA, GDPR, PCI DSS on Exadata
- Data masking for non-production Exadata clones
- Managing multi-tenancy security in Exadata CDBs
- Firewall configuration and secure access patterns
Module 10: Automation and Intelligent Operations - Introduction to Oracle Autonomous Database on Exadata
- Automated patching, updates, and maintenance windows
- Automatic SQL tuning and index recommendations
- Using Machine Learning for performance anomaly detection
- Autonomous Health Framework (AHF) for diagnostics
- Exadata-specific health checks using Exachk
- Automated space management and segment advisor
- Intelligent Infrastructure alerts and recommended actions
- Auto Scaling in Exadata Cloud Service for AI spikes
- Automated backup and retention policies
- Scheduled maintenance insights and risk assessment
- Autonomous Database limitations and Exadata advantages
- Handling mixed workloads: autonomous vs manual tuning
- Automated statistics gathering on large Exadata tables
- Using Oracle Support tools with automated diagnostics upload
Module 11: Integration with AI and Analytics Platforms - Connecting Exadata to Python-based AI training pipelines
- Optimizing JDBC and ODP.NET connections for AI applications
- Using Oracle Machine Learning (OML) within Exadata
- Deploying R and Python scripts inside the database
- OML4SQL: built-in algorithms for in-database AI
- Feature engineering using SQL with large Exadata tables
- Model persistence and versioning in Exadata databases
- Secure data export for external AI training
- Performance of JSON and XML queries on Exadata for AI data
- Using In-Memory Column Store for real-time AI scoring
- Batch scoring at scale using Exadata parallelism
- Integrating with TensorFlow and PyTorch via Oracle Data Miner
- Data sampling strategies for model training from Exadata
- Latency benchmarks for inference query responses
- Securing AI model data access with VPD and TDE
Module 12: Workload Isolation and Resource Management - Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Understanding Oracle optimizer behavior on Exadata
- Ensuring SQL benefits from Smart Scan: query transformation checks
- Identifying full table scans eligible for offloading
- Using SQL Monitoring reports to track Exadata-specific operations
- Tuning joins and filters to maximize storage offload
- Partitioning strategies optimized for Exadata and AI workloads
- Index design considerations: when to use, when to avoid
- Using invisible indexes for safe performance testing
- Adaptive execution plans and Exadata response
- SQL Plan Management: baselines, evolution, and stability
- Handling parallel execution on Exadata: DOP and queuing
- Automatic Degree of Parallelism (Auto DOP) tuning
- Minimizing data movement in RAC environments
- Statistics gathering strategies for large Exadata tables
- Handling skewed data distributions in AI training sets
- Using result caching for repeated AI query patterns
Module 7: Exadata Storage Management - Understanding celldisks, griddisks, and ASM allocation
- Creating and managing ASM disk groups for optimal performance
- Normal vs high redundancy: trade-offs for AI systems
- Mirroring policies in a two-or-more storage server environment
- Provisioning storage for database, recovery, and software areas
- Extending storage capacity: adding storage servers
- Rebalancing ASM disk groups after changes
- Monitoring free space and predicting exhaustion
- Handling cell failures and automatic recovery
- Using FLASHCACHE and FLASHLOG configurations
- Tuning storage indexes for specific query patterns
- Managing Hybrid Columnar Compression at table and partition levels
- Decompressing segments during updates or maintenance
- Moving databases between Exadata systems using backup and restore
- Monitoring cell smart table scan statistics
- Handling temporary and undo tablespaces on Exadata
Module 8: High Availability and Disaster Recovery - Oracle RAC architecture and Exadata integration
- ASM and its role in redundancy and resilience
- Fast disk resync and proactive failure detection
- Handling cell failures: impact and recovery procedures
- Database Flashback and Exadata: configuration and use cases
- Backup strategies: RMAN and Exadata Smart Flash Logging
- Configuring RMAN for optimal Exadata throughput
- Using Incremental Merge Backups to reduce backup windows
- Data Guard integration with Exadata databases
- Active Data Guard and real-time query offload to standby
- Far Sync instances in Exadata environments
- Snapshot Standby: use cases for AI testing and development
- Zero Data Loss Recovery Appliance with Exadata
- Backup compression and encryption on Exadata
- Testing and documenting DR failover procedures
- Validating RPO and RTO under AI workload stress
Module 9: Security and Compliance in Exadata - Database Vault configuration on Exadata
- Data Redaction for sensitive fields in AI datasets
- Transparent Data Encryption (TDE) at column, table, and tablespace levels
- Managing TDE wallets in an Exadata environment
- Secure backup practices with encrypted RMAN
- Auditing strategies: fine-grained and unified auditing
- Implementing row-level security for AI access control
- Monitoring privileged user activity on Exadata
- Patching strategies for security: downtime vs rolling patches
- Applying Exadata system patches using dbaascli and other tools
- Validating security posture with Oracle Configuration Manager
- Compliance frameworks: HIPAA, GDPR, PCI DSS on Exadata
- Data masking for non-production Exadata clones
- Managing multi-tenancy security in Exadata CDBs
- Firewall configuration and secure access patterns
Module 10: Automation and Intelligent Operations - Introduction to Oracle Autonomous Database on Exadata
- Automated patching, updates, and maintenance windows
- Automatic SQL tuning and index recommendations
- Using Machine Learning for performance anomaly detection
- Autonomous Health Framework (AHF) for diagnostics
- Exadata-specific health checks using Exachk
- Automated space management and segment advisor
- Intelligent Infrastructure alerts and recommended actions
- Auto Scaling in Exadata Cloud Service for AI spikes
- Automated backup and retention policies
- Scheduled maintenance insights and risk assessment
- Autonomous Database limitations and Exadata advantages
- Handling mixed workloads: autonomous vs manual tuning
- Automated statistics gathering on large Exadata tables
- Using Oracle Support tools with automated diagnostics upload
Module 11: Integration with AI and Analytics Platforms - Connecting Exadata to Python-based AI training pipelines
- Optimizing JDBC and ODP.NET connections for AI applications
- Using Oracle Machine Learning (OML) within Exadata
- Deploying R and Python scripts inside the database
- OML4SQL: built-in algorithms for in-database AI
- Feature engineering using SQL with large Exadata tables
- Model persistence and versioning in Exadata databases
- Secure data export for external AI training
- Performance of JSON and XML queries on Exadata for AI data
- Using In-Memory Column Store for real-time AI scoring
- Batch scoring at scale using Exadata parallelism
- Integrating with TensorFlow and PyTorch via Oracle Data Miner
- Data sampling strategies for model training from Exadata
- Latency benchmarks for inference query responses
- Securing AI model data access with VPD and TDE
Module 12: Workload Isolation and Resource Management - Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Oracle RAC architecture and Exadata integration
- ASM and its role in redundancy and resilience
- Fast disk resync and proactive failure detection
- Handling cell failures: impact and recovery procedures
- Database Flashback and Exadata: configuration and use cases
- Backup strategies: RMAN and Exadata Smart Flash Logging
- Configuring RMAN for optimal Exadata throughput
- Using Incremental Merge Backups to reduce backup windows
- Data Guard integration with Exadata databases
- Active Data Guard and real-time query offload to standby
- Far Sync instances in Exadata environments
- Snapshot Standby: use cases for AI testing and development
- Zero Data Loss Recovery Appliance with Exadata
- Backup compression and encryption on Exadata
- Testing and documenting DR failover procedures
- Validating RPO and RTO under AI workload stress
Module 9: Security and Compliance in Exadata - Database Vault configuration on Exadata
- Data Redaction for sensitive fields in AI datasets
- Transparent Data Encryption (TDE) at column, table, and tablespace levels
- Managing TDE wallets in an Exadata environment
- Secure backup practices with encrypted RMAN
- Auditing strategies: fine-grained and unified auditing
- Implementing row-level security for AI access control
- Monitoring privileged user activity on Exadata
- Patching strategies for security: downtime vs rolling patches
- Applying Exadata system patches using dbaascli and other tools
- Validating security posture with Oracle Configuration Manager
- Compliance frameworks: HIPAA, GDPR, PCI DSS on Exadata
- Data masking for non-production Exadata clones
- Managing multi-tenancy security in Exadata CDBs
- Firewall configuration and secure access patterns
Module 10: Automation and Intelligent Operations - Introduction to Oracle Autonomous Database on Exadata
- Automated patching, updates, and maintenance windows
- Automatic SQL tuning and index recommendations
- Using Machine Learning for performance anomaly detection
- Autonomous Health Framework (AHF) for diagnostics
- Exadata-specific health checks using Exachk
- Automated space management and segment advisor
- Intelligent Infrastructure alerts and recommended actions
- Auto Scaling in Exadata Cloud Service for AI spikes
- Automated backup and retention policies
- Scheduled maintenance insights and risk assessment
- Autonomous Database limitations and Exadata advantages
- Handling mixed workloads: autonomous vs manual tuning
- Automated statistics gathering on large Exadata tables
- Using Oracle Support tools with automated diagnostics upload
Module 11: Integration with AI and Analytics Platforms - Connecting Exadata to Python-based AI training pipelines
- Optimizing JDBC and ODP.NET connections for AI applications
- Using Oracle Machine Learning (OML) within Exadata
- Deploying R and Python scripts inside the database
- OML4SQL: built-in algorithms for in-database AI
- Feature engineering using SQL with large Exadata tables
- Model persistence and versioning in Exadata databases
- Secure data export for external AI training
- Performance of JSON and XML queries on Exadata for AI data
- Using In-Memory Column Store for real-time AI scoring
- Batch scoring at scale using Exadata parallelism
- Integrating with TensorFlow and PyTorch via Oracle Data Miner
- Data sampling strategies for model training from Exadata
- Latency benchmarks for inference query responses
- Securing AI model data access with VPD and TDE
Module 12: Workload Isolation and Resource Management - Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Introduction to Oracle Autonomous Database on Exadata
- Automated patching, updates, and maintenance windows
- Automatic SQL tuning and index recommendations
- Using Machine Learning for performance anomaly detection
- Autonomous Health Framework (AHF) for diagnostics
- Exadata-specific health checks using Exachk
- Automated space management and segment advisor
- Intelligent Infrastructure alerts and recommended actions
- Auto Scaling in Exadata Cloud Service for AI spikes
- Automated backup and retention policies
- Scheduled maintenance insights and risk assessment
- Autonomous Database limitations and Exadata advantages
- Handling mixed workloads: autonomous vs manual tuning
- Automated statistics gathering on large Exadata tables
- Using Oracle Support tools with automated diagnostics upload
Module 11: Integration with AI and Analytics Platforms - Connecting Exadata to Python-based AI training pipelines
- Optimizing JDBC and ODP.NET connections for AI applications
- Using Oracle Machine Learning (OML) within Exadata
- Deploying R and Python scripts inside the database
- OML4SQL: built-in algorithms for in-database AI
- Feature engineering using SQL with large Exadata tables
- Model persistence and versioning in Exadata databases
- Secure data export for external AI training
- Performance of JSON and XML queries on Exadata for AI data
- Using In-Memory Column Store for real-time AI scoring
- Batch scoring at scale using Exadata parallelism
- Integrating with TensorFlow and PyTorch via Oracle Data Miner
- Data sampling strategies for model training from Exadata
- Latency benchmarks for inference query responses
- Securing AI model data access with VPD and TDE
Module 12: Workload Isolation and Resource Management - Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Database Resource Manager (DBRM) configuration
- I/O Resource Manager (IORM) policies and plans
- Defining consumer groups and their Exadata priorities
- Mixing OLTP, batch, and AI workloads safely
- Setting I/O minimums, maximums, and allocation share
- Monitoring resource plan effectiveness using OEM
- Tuning inter-database plans in multitenant environments
- Handling runaway queries and automatic throttling
- Setting up active session pools for AI jobs
- Automated emergency resource reallocation
- Testing resource plans under synthetic AI loads
- Integrating with Oracle Cloud Service Level Agreements
- Capacity planning with resource manager constraints
- Auditing resource usage across departments and teams
- Reporting on resource consumption for chargeback models
Module 13: Cloud and Hybrid Operations - Exadata Cloud Service architecture and provisioning
- Migrating on-prem databases to Exadata Cloud Service
- Using Data Guard between on-prem and cloud Exadata
- Cross-region disaster recovery strategies
- Bandwidth planning for hybrid data movement
- Security and encryption across hybrid environments
- Unified monitoring using OEM Cloud Control
- Cost optimization: compute scaling and auto-stop features
- Cloud automation with Terraform and Oracle CLI
- API-driven management of Exadata Cloud resources
- Backup to Object Storage and FastRecovery
- Integrating with OCI Identity and Access Management
- Network Peering and Private Endpoints for secure access
- Performance tuning in cloud vs on-premises
- Vendor lock-in considerations and exit strategies
Module 14: Advanced Tuning and Scalability - Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Massive concurrency handling on Exadata
- Memory management: SGA, PGA, and In-Memory allocation
- TPC-C and TPC-H benchmarking on Exadata
- Scaling out with additional RAC nodes
- NUMA topology awareness in Exadata servers
- Reducing parse overhead in AI-driven applications
- Connection pooling with WebLogic and UCP
- Scaling storage with Exadata Storage Expansion Racks
- Handling petabyte-scale data warehouses on Exadata
- Patch and upgrade planning for minimal downtime
- Rolling upgrades for RAC and storage cells
- Testing failover and performance post-upgrade
- Long-term retention and archival strategies
- Tuning for time-series data used in AI models
- Optimizing ETL processes running on Exadata
Module 15: Professional Practice and Real-World Projects - Diagnosing a simulated Exadata performance crisis
- Designing a secure Exadata environment for a financial AI app
- Creating a disaster recovery plan with documented RPO/RTO
- Optimizing a slow AI query using Smart Scan analysis
- Implementing hybrid columnar compression on a large dataset
- Configuring IORM to protect OLTP during AI batch runs
- Setting up encrypted backups to cloud storage
- Automating health checks and reporting
- Documenting a complete Exadata deployment plan
- Presenting a tuning recommendation with before/after metrics
- Building a resource isolation model for mixed workloads
- Planning a migration from non-Exadata to Exadata
- Creating a monitoring dashboard for CIO-level reporting
- Simulating a cell failure and validating recovery
- Generating a security audit report for compliance
- Integrating Exadata with a CI/CD pipeline
Module 16: Career Advancement and Certification Preparation - Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review
- Positioning your Exadata expertise in the job market
- Building a portfolio of Exadata tuning projects
- Highlighting AI-readiness skills on your resume
- Preparing for Oracle certification exams related to Exadata
- Understanding Oracle 1Z0-082, 1Z0-083, and cluster exam topics
- Leveraging the Certificate of Completion in interviews
- Joining Oracle user groups and Exadata communities
- Negotiating salary increases based on new competencies
- Transitioning from general DBA to Exadata specialist
- Becoming a trusted advisor for AI infrastructure decisions
- Documenting ROI from Exadata optimizations
- Presenting technical findings to non-technical stakeholders
- Developing thought leadership through blogs and case studies
- Networking with Oracle partners and cloud architects
- Planning your next learning path in Oracle Cloud and AI
- Final assessment: comprehensive Exadata proficiency review