Datawarehousing Course Syllabus
Course Syllabus
Download SyllabusModule 1: Introduction to Data Warehousing
- Fundamentals of Data Warehousing
- Importance and Benefits of Data Warehousing
- Data Warehousing vs. Traditional Databases
- Key Components of a Data Warehouse
Module 2: Data Warehouse Architecture
- Data Warehouse Layers (ETL, Storage, Presentation)
- Enterprise vs. Cloud Data Warehousing
- Data Marts and Operational Data Stores (ODS)
- OLTP vs. OLAP Systems
Module 3: Data Modeling in Data Warehousing
- Conceptual, Logical, and Physical Data Models
- Dimensional Modeling: Star Schema & Snowflake Schema
- Fact and Dimension Tables
- Slowly Changing Dimensions (SCD)
Module 4: Extract, Transform, Load (ETL) Process
- Introduction to ETL and Data Integration
- ETL Tools and Technologies (Informatica, Talend, SSIS)
- Data Extraction Techniques (Batch, Real-Time)
- Data Cleaning, Transformation, and Loading Methods
Module 5: Data Warehouse Implementation
- Designing a Data Warehouse
- Data Partitioning and Indexing Strategies
- Performance Optimization Techniques
- Data Governance and Metadata Management
Module 6: Business Intelligence and Reporting
- Introduction to Business Intelligence (BI)
- BI Tools (Power BI, Tableau, Looker)
- Dashboard and Report Development
- Data Visualization Best Practices
Module 7: Data Warehouse Performance Tuning
- Query Optimization Strategies
- Indexing and Partitioning for Performance
- Caching and Materialized Views
- Handling Large-Scale Data Processing
Module 8: Cloud Data Warehousing
- Introduction to Cloud-Based Data Warehouses
- AWS Redshift, Google BigQuery, and Snowflake
- Cloud Storage and Security Considerations
- Cost Optimization in Cloud Data Warehousing
Module 9: Data Governance and Security
- Data Quality and Master Data Management (MDM)
- Data Security Policies and Compliance (GDPR, HIPAA)
- Access Control and Encryption Techniques
- Auditing and Monitoring Data Warehouse Usage
Module 10: Big Data and Advanced Analytics
- Integration of Data Warehouses with Big Data Technologies
- Hadoop, Spark, and NoSQL for Data Warehousing
- Machine Learning and AI in Data Warehousing
- Real-Time Data Processing and Streaming Analytics
Module 11: Capstone Project and Career Preparation
- Hands-on Project: Designing and Implementing a Data Warehouse
- Resume Building and Certification Guidance (AWS, Snowflake, Microsoft, Google)
- Interview Preparation and Industry Trends
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