Datawarehousing Course Syllabus
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The aim of our data warehousing syllabus is to get students ready for lucrative employment in data warehousing and business intelligence. Employers are looking for skills that students will acquire, such as proficiency with data warehousing techniques, ETL procedures, and data warehousing tool usage. The data warehouse course outline will give students a solid basis for obtaining more advanced certifications and establishing a prosperous career in the rapidly expanding data analytics industry.
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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