Mastering Data Warehousing

1.Introduction to Data Warehousing
1.1.Understanding the Need for Data Warehousing
1.2.The Evolution of Data Warehousing
1.3.Key Concepts and Terminology
1.4.Benefits and Challenges of Data Warehousing
2.Data Warehouse Architecture
2.1.Components of a Data Warehouse
2.2.Data Warehouse Models: Inmon vs. Kimball
2.3.Extract, Transform, Load (ETL) Processes
2.4.Data Warehousing in the Cloud
3.Designing a Data Warehouse
3.1.Dimensional Modeling Concepts
3.2.Fact and Dimension Tables
3.3.Star Schema and Snowflake Schema
3.4.Slowly Changing Dimensions (SCD)
4.ETL and Data Integration
4.1.ETL Workflow and Processes
4.2.Data Transformation Techniques
4.3.Data Cleansing and Quality Assurance
4.4.Change Data Capture (CDC) Strategies
5.Data Warehouse Storage and Management
5.1.Choosing the Right Storage Solutions
5.2.Data Compression and Partitioning
5.3.Indexing Strategies for Performance
5.4.Data Archiving and Retention Policies
6.Data Warehouse Querying and Optimization
6.1.SQL Basics for Data Warehousing
6.2.Query Optimization Techniques
6.3.Materialized Views and Query Performance
6.4.Parallel Processing and In-Memory Databases
7.Data Warehousing for Business Intelligence
7.1.Enabling Business Decision-Making
7.2.Data Visualization and Reporting Tools
7.3.Self-Service BI and Dashboards
7.4.Advanced Analytics in the Data Warehouse
8.Data Security and Governance in Data Warehousing
8.1.Ensuring Data Privacy and Security
8.2.Data Governance Frameworks
8.3.Auditing and Compliance in Data Warehousing
8.4.Data Lineage and Metadata Management
9.Scalability and Performance Tuning
9.1.Scaling Up vs. Scaling Out
9.2.Data Warehouse Performance Bottlenecks
9.3.Monitoring and Tuning for Performance
9.4.Predictive Analytics for Workload Management
10.Advanced Data Warehousing Concepts
10.1.Data Warehousing in Real-Time
10.2.Federated Data Warehousing
10.3.NoSQL and Unstructured Data in the Warehouse
10.4.Geospatial and Temporal Data Warehousing
11.Data Warehousing in Industry Domains
11.1.Healthcare Data Warehousing
11.2.Retail and E-commerce Data Warehousing
11.3.Financial Services Data Warehousing
11.4.Manufacturing and Supply Chain Data Warehousing
12.Data Warehousing for Big Data and IoT
12.1.Integrating Big Data into Data Warehousing
12.2.Data Warehousing Challenges in IoT
12.3.Processing and Analyzing Streaming Data in the Warehouse
12.4.Building Data Lakes with Data Warehousing
13.Data Warehousing Best Practices
13.1.Collaboration between IT and Business Users
13.2.Data Warehousing Project Management
13.3.Common Mistakes and How to Avoid Them
13.4.Future-Proofing Your Data Warehouse
14.Future Trends in Data Warehousing
14.1.Data Warehousing in the Age of AI and ML
14.2.Quantum Computing Impact on Data Warehousing
14.3.Hybrid Cloud Data Warehousing
14.4.Ethical Considerations in Data Warehousing
15.Building Your Data Warehousing Strategy
15.1.Assessing Business Needs and Goals
15.2.Selecting the Right Tools and Technologies
15.3.Creating a Roadmap for Successful Data Warehousing
16.Appendix
16.1.Glossary of Data Warehousing Terms
16.2.Recommended Tools and Resources
16.3.Interviews with Data Warehousing Experts
16.4.Sample Data Warehousing Project Plan
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