01
Unit 1: Data Warehousing Concepts and Architecture
Operational Data Store (ODS) vs. data warehouse, Bill Inmon vs. Ralph Kimball approaches (normalized vs. star schema), Data warehouse architecture (staging, ETL, presentation layer), conformed dimensions and fact granularity, Slowly Changing Dimensions (SCD Type 1-6), Surrogate keys and dimensionality modeling.
02
Unit 2: OLAP and Multidimensional Analysis
Online Analytical Processing (OLAP) operations (slice/dice, drill-down/up, pivot), MOLAP vs. ROLAP vs. HOLAP, OLAP cube construction and sparse cube optimization, Bitmap indexing for high-cardinality dimensions, Aggregation hierarchies and roll-up strategies, Materialized views and summary tables.
03
Unit 3: BI Tools and Visualization
Self-service BI platforms (Tableau, Power BI, Looker), Dashboard design principles and storytelling, KPI definition and cascading, Advanced visualizations (small multiples, sparklines, heatmaps), Geographic visualization and custom polygons, Interactive filtering and parameter actions, Embedded analytics and white-labeling.
04
Unit 4: ETL/ELT Pipelines and Data Quality
Extract-Transform-Load vs. Extract-Load-Transform paradigms, Change Data Capture (CDC) techniques (log-based, timestamp, triggers), Data quality dimensions (accuracy, completeness, consistency, timeliness), Data profiling and anomaly detection, Master Data Management (MDM) strategies, Data lineage and impact analysis.
05
Unit 5: Modern Data Platforms and Lakehouse
Data lake architectures and schema-on-read challenges, Lakehouse paradigm (Delta Lake, Apache Iceberg, Hudi), Semantic layer abstraction (dbt, LookML), Headless BI and metric stores (Metrics Layer), Real-time analytics (Kafka Streams, Flink SQL), Federated query engines (Presto, Trino, Athena).