Data Warehouse Design + Data pipeline

মডিউল – ১ : ডেটা ওয়্যারহাউজ পরিচিতি (Introduction to Data Warehouse)

• Data Warehouse কী
  
• OLTP vs OLAP
  
• Analytics Workflow
  
• Why Data Warehouse?
• Business Analytics Use Cases

মডিউল – ২ : ডেটা মডেলিং Fundamentals (Data Modeling Basics)

• ER Model Basics
  
• Normalization
  
• OLTP Modeling
  
• Conceptual, Logical & Physical Model
  
• Important Modeling Tools (Erwin, Draw.io, Lucidchart)

মডিউল – ৩ : OLAP Data Modeling (Dimensional Modeling)

• Star Schema
  
• Snowflake Schema
  
• Fact Table vs Dimension Table
  
• Grain
  
• Additive, Semi-additive, Non-additive
  
• Surrogate Keys
  
• Junk Dimensions
  
• Degenerate Dimensions

মডিউল – ৪ : Fact Table Design

• Transaction Fact
  
• Snapshot Fact
  
• Periodic Snapshot Fact
  
• Accumulating Snapshot Fact
  
• Factless Fact
  
• When to use which Fact type
  
• Best Practices
  

মডিউল – ৫ : Dimension Table Design

• Role-playing dimensions
  
• Conformed dimensions
  
• Slowly Changing Dimensions
  
  • SCD Type 0
    
  • SCD Type 1
    
  • SCD Type 2
    
  • SCD Type 3
    
• Hierarchies
  
• Attributes Best Practices

মডিউল – ৬ : Data Warehouse Architecture

• Inmon vs Kimball Approach
  
• Modern DW Architecture
  
• Staging → ODS → EDW → Data Mart
  
• Data Lake vs Data Warehouse
  
• Lakehouse Concept
  
• On-prem vs Cloud DW

মডিউল – ৭ : ETL / ELT for Data Warehouse

• ETL vs ELT
  
• Data Extraction (DB, API, Files)
  
• Staging Area Design
  
• Transformation Logic
  
• Loading Strategy
  
• Incremental Load vs Full Load
  
• Change Data Capture (CDC)
• Error Logging & Audit

মডিউল – ৮ : SQL for Data Warehouse (DW SQL)

• Window Functions
  
• CTE
  
• Complex Join
  
• Date Dimension Generation
  
• SCD Type-2 SQL Logic
  
• Merge Statement
  
• SQL Optimization & Indexing

মডিউল – ৯ : Data Quality & Validation

• Data Profiling
  
• Data Quality Rules
  
• Business Rules Validation
  
• Duplicate Handling
  
• Null Handling
  
• Reconciliation
  
• Audit Fields Design

মডিউল – ১০ : Performance Optimization

• Data Partitioning
  
• Indexing
  
• Materialized Views
  
• Caching
  
• Data Skew Handlin
  
• Compression Techniques

মডিউল – ১১ : BI Tools & Data Warehouse Consumption Layer

• Semantic Layer Design
  
• Power BI Data Modeling
  
• SSAS / Tabular Model
  
• Dashboard Optimization
• KPI Design

মডিউল – ১২ : Data Warehouse Project (End-to-End Case Study)

Project Components:

1. Requirement Analysis
   
2. Dimensional Modeling
   
3. Star Schema Design
   
4. ETL Pipeline Build
   
5. DW Deployment (Cloud)
   
6. Power BI Dashboard
   
7. Documentation

Industries:

• Sales
  
• Banking
  
• Telecom
• E-commerce

মডিউল – ১৩ : Interview & Certification Preparation

• DW Architect Interview Questions
  
• Modeling Whiteboard Practice
  
• SQL Test
  
• Case Study Presentation
  
• Resume Preparation