Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-spectrum-discover-advanced-video-course Lesson 1: Introduction to IBM Spectrum Discover
1.1 Overview of IBM Spectrum Discover
1.2 Key Features and Benefits
1.3 Use Cases and Applications
1.4 System Requirements
1.5 Installation Prerequisites
1.6 Basic Architecture
1.7 Initial Setup and Configuration
1.8 User Interface Tour
1.9 Navigating the Dashboard
1.10 Hands-On: First Login and Exploration
Lesson 2: Data Management Fundamentals
2.1 Data Lifecycle Management
2.2 Data Governance Principles
2.3 Data Classification Techniques
2.4 Metadata Management
2.5 Data Lineage and Provenance
2.6 Data Quality and Integrity
2.7 Data Security and Compliance
2.8 Data Retention Policies
2.9 Data Archiving Strategies
2.10 Hands-On: Creating a Data Management Plan
Lesson 3: Advanced Data Discovery Techniques
3.1 Data Discovery Workflows
3.2 Automated Data Discovery Tools
3.3 Data Profiling and Analysis
3.4 Data Cataloging and Indexing
3.5 Data Search and Query Optimization
3.6 Data Visualization Techniques
3.7 Data Anomaly Detection
3.8 Data Enrichment and Transformation
3.9 Data Integration and Federation
3.10 Hands-On: Performing Advanced Data Discovery
Lesson 4: Metadata Management and Cataloging
4.1 Metadata Standards and Frameworks
4.2 Metadata Capture and Storage
4.3 Metadata Enrichment and Tagging
4.4 Metadata Search and Retrieval
4.5 Metadata Governance and Compliance
4.6 Metadata Lineage and Impact Analysis
4.7 Metadata Interoperability and Exchange
4.8 Metadata Quality and Consistency
4.9 Metadata Versioning and History
4.10 Hands-On: Implementing a Metadata Catalog
Lesson 5: Data Governance and Compliance
5.1 Data Governance Frameworks
5.2 Data Policy and Procedure Development
5.3 Data Access Control and Permissions
5.4 Data Audit and Monitoring
5.5 Data Privacy and Protection
5.6 Data Regulatory Compliance
5.7 Data Risk Management
5.8 Data Incident Response Planning
5.9 Data Ethics and Responsible Use
5.10 Hands-On: Developing a Data Governance Plan
Lesson 6: Data Lineage and Impact Analysis
6.1 Data Lineage Concepts and Importance
6.2 Data Lineage Capture and Visualization
6.3 Data Impact Analysis Techniques
6.4 Data Dependency and Relationship Mapping
6.5 Data Change Management and Tracking
6.6 Data Lineage and Compliance Reporting
6.7 Data Lineage and Data Quality Improvement
6.8 Data Lineage and Data Integration
6.9 Data Lineage and Data Security
6.10 Hands-On: Conducting a Data Lineage Analysis
Lesson 7: Data Quality and Integrity Management
7.1 Data Quality Dimensions and Metrics
7.2 Data Quality Assessment and Profiling
7.3 Data Quality Improvement Techniques
7.4 Data Quality Monitoring and Reporting
7.5 Data Quality Governance and Standards
7.6 Data Quality and Data Integration
7.7 Data Quality and Data Migration
7.8 Data Quality and Data Analytics
7.9 Data Quality and Data Compliance
7.10 Hands-On: Implementing a Data Quality Framework
Lesson 8: Data Security and Access Control
8.1 Data Security Fundamentals
8.2 Data Access Control Models
8.3 Data Encryption and Tokenization
8.4 Data Masking and Anonymization
8.5 Data Security Policies and Procedures
8.6 Data Security Audit and Monitoring
8.7 Data Security Incident Response
8.8 Data Security and Compliance
8.9 Data Security and Data Governance
8.10 Hands-On: Configuring Data Security Settings
Lesson 9: Data Retention and Archiving Strategies
9.1 Data Retention Policies and Procedures
9.2 Data Archiving Techniques and Tools
9.3 Data Retention and Compliance
9.4 Data Retention and Data Governance
9.5 Data Retention and Data Security
9.6 Data Retention and Data Quality
9.7 Data Retention and Data Analytics
9.8 Data Retention and Data Integration
9.9 Data Retention and Data Migration
9.10 Hands-On: Implementing a Data Retention Policy
Lesson 10: Data Integration and Federation
10.1 Data Integration Concepts and Techniques
10.2 Data Federation and Virtualization
10.3 Data Integration and Data Quality
10.4 Data Integration and Data Governance
10.5 Data Integration and Data Security
10.6 Data Integration and Data Analytics
10.7 Data Integration and Data Migration
10.8 Data Integration and Data Compliance
10.9 Data Integration Tools and Platforms
10.10 Hands-On: Performing Data Integration Tasks
Lesson 11: Data Analytics and Business Intelligence
11.1 Data Analytics Fundamentals
11.2 Business Intelligence Tools and Techniques
11.3 Data Visualization and Reporting
11.4 Data Analytics and Data Quality
11.5 Data Analytics and Data Governance
11.6 Data Analytics and Data Security
11.7 Data Analytics and Data Integration
11.8 Data Analytics and Data Compliance
11.9 Data Analytics and Data Migration
11.10 Hands-On: Conducting Data Analytics Projects
Lesson 12: Data Migration and Transformation
12.1 Data Migration Strategies and Best Practices
12.2 Data Transformation Techniques and Tools
12.3 Data Migration and Data Quality
12.4 Data Migration and Data Governance
12.5 Data Migration and Data Security
12.6 Data Migration and Data Integration
12.7 Data Migration and Data Compliance
12.8 Data Migration and Data Analytics
12.9 Data Migration and Data Retention
12.10 Hands-On: Executing a Data Migration Project
Lesson 13: Data Compliance and Regulatory Management
13.1 Data Compliance Frameworks and Standards
13.2 Data Regulatory Requirements and Reporting
13.3 Data Compliance and Data Governance
13.4 Data Compliance and Data Security
13.5 Data Compliance and Data Quality
13.6 Data Compliance and Data Integration
13.7 Data Compliance and Data Analytics
13.8 Data Compliance and Data Migration
13.9 Data Compliance and Data Retention
13.10 Hands-On: Implementing Data Compliance Measures
Lesson 14: Data Ethics and Responsible Data Use
14.1 Data Ethics Principles and Frameworks
14.2 Responsible Data Use Policies and Procedures
14.3 Data Ethics and Data Governance
14.4 Data Ethics and Data Security
14.5 Data Ethics and Data Quality
14.6 Data Ethics and Data Integration
14.7 Data Ethics and Data Analytics
14.8 Data Ethics and Data Compliance
14.9 Data Ethics and Data Migration
14.10 Hands-On: Developing a Data Ethics Policy
Lesson 15: Advanced Data Governance Techniques
15.1 Advanced Data Governance Frameworks
15.2 Data Governance Maturity Models
15.3 Data Governance Tools and Platforms
15.4 Data Governance and Data Quality
15.5 Data Governance and Data Security
15.6 Data Governance and Data Integration
15.7 Data Governance and Data Analytics
15.8 Data Governance and Data Compliance
15.9 Data Governance and Data Migration
15.10 Hands-On: Implementing Advanced Data Governance
Lesson 16: Data Lake Management and Optimization
16.1 Data Lake Architecture and Design
16.2 Data Lake Ingestion and Storage
16.3 Data Lake Governance and Security
16.4 Data Lake Data Quality and Integration
16.5 Data Lake Data Analytics and Reporting
16.6 Data Lake Data Retention and Archiving
16.7 Data Lake Data Compliance and Ethics
16.8 Data Lake Performance Optimization
16.9 Data Lake Cost Management
16.10 Hands-On: Managing a Data Lake Environment
Lesson 17: Data Warehouse Management and Optimization
17.1 Data Warehouse Architecture and Design
17.2 Data Warehouse ETL Processes
17.3 Data Warehouse Governance and Security
17.4 Data Warehouse Data Quality and Integration
17.5 Data Warehouse Data Analytics and Reporting
17.6 Data Warehouse Data Retention and Archiving
17.7 Data Warehouse Data Compliance and Ethics
17.8 Data Warehouse Performance Optimization
17.9 Data Warehouse Cost Management
17.10 Hands-On: Managing a Data Warehouse Environment
Lesson 18: Big Data Management and Analytics
18.1 Big Data Fundamentals and Technologies
18.2 Big Data Ingestion and Storage
18.3 Big Data Governance and Security
18.4 Big Data Quality and Integration
18.5 Big Data Analytics and Reporting
18.6 Big Data Retention and Archiving
18.7 Big Data Compliance and Ethics
18.8 Big Data Performance Optimization
18.9 Big Data Cost Management
18.10 Hands-On: Conducting Big Data Analytics Projects
Lesson 19: Machine Learning and AI Integration
19.1 Machine Learning Fundamentals
19.2 AI Integration with Data Management
19.3 AI and Data Governance
19.4 AI and Data Security
19.5 AI and Data Quality
19.6 AI and Data Integration
19.7 AI and Data Analytics
19.8 AI and Data Compliance
19.9 AI and Data Ethics
19.10 Hands-On: Implementing AI in Data Management
Lesson 20: Cloud Data Management and Migration
20.1 Cloud Data Management Fundamentals
20.2 Cloud Data Migration Strategies
20.3 Cloud Data Governance and Security
20.4 Cloud Data Quality and Integration
20.5 Cloud Data Analytics and Reporting
20.6 Cloud Data Retention and Archiving
20.7 Cloud Data Compliance and Ethics
20.8 Cloud Data Performance Optimization
20.9 Cloud Data Cost Management
20.10 Hands-On: Executing Cloud Data Migration Projects
Lesson 21: Hybrid Data Management and Integration
21.1 Hybrid Data Management Architecture
21.2 Hybrid Data Integration Techniques
21.3 Hybrid Data Governance and Security
21.4 Hybrid Data Quality and Integration
21.5 Hybrid Data Analytics and Reporting
21.6 Hybrid Data Retention and Archiving
21.7 Hybrid Data Compliance and Ethics
21.8 Hybrid Data Performance Optimization
21.9 Hybrid Data Cost Management
21.10 Hands-On: Implementing Hybrid Data Management
Lesson 22: Real-Time Data Management and Analytics
22.1 Real-Time Data Management Fundamentals
22.2 Real-Time Data Ingestion and Processing
22.3 Real-Time Data Governance and Security
22.4 Real-Time Data Quality and Integration
22.5 Real-Time Data Analytics and Reporting
22.6 Real-Time Data Retention and Archiving
22.7 Real-Time Data Compliance and Ethics
22.8 Real-Time Data Performance Optimization
22.9 Real-Time Data Cost Management
22.10 Hands-On: Conducting Real-Time Data Analytics Projects
Lesson 23: DataOps and Agile Data Management
23.1 DataOps Fundamentals and Principles
23.2 Agile Data Management Practices
23.3 DataOps and Data Governance
23.4 DataOps and Data Security
23.5 DataOps and Data Quality
23.6 DataOps and Data Integration
23.7 DataOps and Data Analytics
23.8 DataOps and Data Compliance
23.9 DataOps and Data Ethics
23.10 Hands-On: Implementing DataOps in Data Management
Lesson 24: Data Science and Advanced Analytics
24.1 Data Science Fundamentals
24.2 Advanced Analytics Techniques
24.3 Data Science and Data Governance
24.4 Data Science and Data Security
24.5 Data Science and Data Quality
24.6 Data Science and Data Integration
24.7 Data Science and Data Compliance
24.8 Data Science and Data Ethics
24.9 Data Science Tools and Platforms
24.10 Hands-On: Conducting Data Science Projects
Lesson 25: Data Engineering and Pipeline Management
25.1 Data Engineering Fundamentals
25.2 Data Pipeline Design and Management
25.3 Data Engineering and Data Governance
25.4 Data Engineering and Data Security
25.5 Data Engineering and Data Quality
25.6 Data Engineering and Data Integration
25.7 Data Engineering and Data Analytics
25.8 Data Engineering and Data Compliance
25.9 Data Engineering Tools and Platforms
25.10 Hands-On: Implementing Data Pipelines
Lesson 26: Data Virtualization and Federation
26.1 Data Virtualization Concepts and Techniques
26.2 Data Federation Architecture and Design
26.3 Data Virtualization and Data Governance
26.4 Data Virtualization and Data Security
26.5 Data Virtualization and Data Quality
26.6 Data Virtualization and Data Integration
26.7 Data Virtualization and Data Analytics
26.8 Data Virtualization and Data Compliance
26.9 Data Virtualization Tools and Platforms
26.10 Hands-On: Implementing Data Virtualization
Lesson 27: Data Cataloging and Metadata Management
27.1 Data Cataloging Fundamentals
27.2 Metadata Management Best Practices
27.3 Data Cataloging and Data Governance
27.4 Data Cataloging and Data Security
27.5 Data Cataloging and Data Quality
27.6 Data Cataloging and Data Integration
27.7 Data Cataloging and Data Analytics
27.8 Data Cataloging and Data Compliance
27.9 Data Cataloging Tools and Platforms
27.10 Hands-On: Implementing a Data Catalog
Lesson 28: Data Privacy and Protection
28.1 Data Privacy Fundamentals
28.2 Data Protection Techniques and Tools
28.3 Data Privacy and Data Governance
28.4 Data Privacy and Data Security
28.5 Data Privacy and Data Quality
28.6 Data Privacy and Data Integration
28.7 Data Privacy and Data Analytics
28.8 Data Privacy and Data Compliance
28.9 Data Privacy and Data Ethics
28.10 Hands-On: Implementing Data Privacy Measures
Lesson 29: Data Monetization and Value Creation
29.1 Data Monetization Strategies
29.2 Value Creation from Data Assets
29.3 Data Monetization and Data Governance
29.4 Data Monetization and Data Security
29.5 Data Monetization and Data Quality
29.6 Data Monetization and Data Integration
29.7 Data Monetization and Data Analytics
29.8 Data Monetization and Data Compliance
29.9 Data Monetization and Data Ethics
29.10 Hands-On: Developing a Data Monetization Plan
Lesson 30: Data Lifecycle Management and Optimization
30.1 Data Lifecycle Management Fundamentals
30.2 Data Lifecycle Optimization Techniques
30.3 Data Lifecycle and Data Governance
30.4 Data Lifecycle and Data Security
30.5 Data Lifecycle and Data Quality
30.6 Data Lifecycle and Data Integration
30.7 Data Lifecycle and Data Analytics
30.8 Data Lifecycle and Data Compliance
30.9 Data Lifecycle and Data Ethics
30.10 Hands-On: Implementing Data Lifecycle Management
Lesson 31: Advanced Data Security and Protection
31.1 Advanced Data Security Techniques
31.2 Data Protection and Encryption
31.3 Data Security and Governance
31.4 Data Security and Compliance
31.5 Data Security and Data Quality
31.6 Data Security and Data Integration
31.7 Data Security and Data Analytics
31.8 Data Security and Data Ethics
31.9 Data Security Tools and Platforms
31.10 Hands-On: Implementing Advanced Data Security
Lesson 32: Data Integration and Interoperability
32.1 Data Integration Challenges and Solutions
32.2 Data Interoperability Standards and Protocols
32.3 Data Integration and Governance
32.4 Data Integration and Security
32.5 Data Integration and Quality
32.6 Data Integration and Analytics
32.7 Data Integration and Compliance
32.8 Data Integration and Ethics
32.9 Data Integration Tools and Platforms
32.10 Hands-On: Executing Data Integration Projects
Lesson 33: Data Quality Management and Improvement
33.1 Data Quality Management Best Practices
33.2 Data Quality Improvement Techniques
33.3 Data Quality and Governance
33.4 Data Quality and Security
33.5 Data Quality and Integration
33.6 Data Quality and Analytics
33.7 Data Quality and Compliance
33.8 Data Quality and Ethics
33.9 Data Quality Tools and Platforms
33.10 Hands-On: Implementing Data Quality Improvement
Lesson 34: Data Compliance and Regulatory Reporting
34.1 Data Compliance Frameworks and Standards
34.2 Regulatory Reporting Requirements
34.3 Data Compliance and Governance
34.4 Data Compliance and Security
34.5 Data Compliance and Quality
34.6 Data Compliance and Integration
34.7 Data Compliance and Analytics
34.8 Data Compliance and Ethics
34.9 Data Compliance Tools and Platforms
34.10 Hands-On: Implementing Data Compliance Reporting
Lesson 35: Data Ethics and Responsible AI
35.1 Data Ethics Principles and Frameworks
35.2 Responsible AI Development and Use
35.3 Data Ethics and Governance
35.4 Data Ethics and Security
35.5 Data Ethics and Quality
35.6 Data Ethics and Integration
35.7 Data Ethics and Analytics
35.8 Data Ethics and Compliance
35.9 Data Ethics Tools and Platforms
35.10 Hands-On: Developing a Responsible AI Policy
Lesson 36: Advanced Data Governance and Stewardship
36.1 Advanced Data Governance Frameworks
36.2 Data Stewardship Roles and Responsibilities
36.3 Data Governance and Security
36.4 Data Governance and Quality
36.5 Data Governance and Integration
36.6 Data Governance and Analytics
36.7 Data Governance and Compliance
36.8 Data Governance and Ethics
36.9 Data Governance Tools and Platforms
36.10 Hands-On: Implementing Advanced Data Governance
Lesson 37: Data Lake Architecture and Design
37.1 Data Lake Architecture Fundamentals
37.2 Data Lake Design Best Practices
37.3 Data Lake Governance and Security
37.4 Data Lake Data Quality and Integration
37.5 Data Lake Data Analytics and Reporting
37.6 Data Lake Data Retention and Archiving
37.7 Data Lake Data Compliance and Ethics
37.8 Data Lake Performance Optimization
37.9 Data Lake Cost Management
37.10 Hands-On: Designing a Data Lake Architecture
Lesson 38: Data Warehouse Architecture and Design
38.1 Data Warehouse Architecture Fundamentals
38.2 Data Warehouse Design Best Practices
38.3 Data Warehouse Governance and Security
38.4 Data Warehouse Data Quality and Integration
38.5 Data Warehouse Data Analytics and Reporting
38.6 Data Warehouse Data Retention and Archiving
38.7 Data Warehouse Data Compliance and Ethics
38.8 Data Warehouse Performance Optimization
38.9 Data Warehouse Cost Management
38.10 Hands-On: Designing a Data Warehouse Architecture
Lesson 39: Big Data Architecture and Design
39.1 Big Data Architecture Fundamentals
39.2 Big Data Design Best Practices
39.3 Big Data Governance and Security
39.4 Big Data Quality and Integration
39.5 Big Data Analytics and Reporting
39.6 Big Data Retention and Archiving
39.7 Big Data Compliance and Ethics
39.8 Big Data Performance Optimization
39.9 Big Data Cost Management
39.10 Hands-On: Designing a Big Data Architecture
Lesson 40: Future Trends in Data Management
40.1 Emerging Technologies in Data Management
40.2 Future of Data Governance and Compliance
40.3 Future of Data Security and Privacy
40.4 Future of Data Quality and Integration
40.5 Future of Data Analytics and AI
40.6 Future of Data Ethics and Responsible Use
40.7 Future of Data Monetization and Value Creation
40.8 Future of Data Lifecycle Management
40.9 Future of Data Management Tools and Platforms