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    📒 Data Warehousing — Syllabus

    datawarehousesyllabus

    ✅ Chapters

    • 01 - Business Intelligence, Analytics, AI & ML

      • BI definition & steps, Analytics, AI, ML types (Supervised, Unsupervised, Semi-supervised, Reinforcement), Deep Learning

    • 02 - OLTP vs OLAP

      • Definitions, differences (purpose, operations, data type, users, performance), OLAP use cases

    • 03 - Data Warehousing Basics

      • Definition, 4 characteristics (Subject-oriented, Integrated, Time-variant, Non-volatile), why DW is needed

    • 04 - Data Warehouse Components

      • Data sources, Staging area, ETL, DW, Data marts, OLAP/reporting layer, Metadata layer

    • 05 - Data Architectures

      • EDW, Data Marts, Data Lake, Lakehouse, Data Mesh, Medallion Architecture, Virtual DW, DW vs EDW

    • 06 - Data Value Chain

      • Data generation → processing → analysis → usage

    • 07 - ETL & ELT

      • ETL vs ELT (timing, scalability, use cases), tools (Informatica, DBT, AWS Glue)

    • 08 - Data Extraction

      • Techniques (DB, API, Web scraping), data types (Structured, Semi-structured, Unstructured)

    • 09 - Data Loading

      • Types (Batch, Real-time, Streaming, CDC), concepts (scheduling, freshness, incremental loads)

    • 10 - Data Transformation

      • Cleaning, standardization, mapping, aggregation, enrichment, filtering, validation, encoding

    • 11 - Reporting

      • Tools (Power BI, Tableau, Qlik), features, Reports vs Dashboards

    • 12 - Data Governance

      • Definition, importance (security, compliance), roles, tools

    • 13 - Master Data Management (MDM)

      • Definition, single source of truth, capabilities (cleansing, integration, sync, version control)

    • 14 - Data Quality

      • Why it's needed, key issues, 6 quality elements, data flow checks, business rule validation

    • 15 - Dimensional Modeling (Core)

      • Facts (definition, grain, structure), types of facts (additive, semi, non-additive), types of fact tables, dimensions

    • 16 - Schemas & Relationships

      • Star Schema, Snowflake Schema, Star vs Snowflake, Cardinality (1:M, 1:1, M:M)

    • 17 - Dimensions Deep Dive

      • Types (Primary, Degenerate, Secondary, Conformed), SCD (Type 1/2/3), Advanced dimensions, Hierarchies

    • 18 - Modeling Process & Design

      • CDM, LDM, PDM, Dimensional modeling steps (grain → dimensions → facts → surrogate keys), Bus Matrix

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