1. Course Syllabus

   1. Module 1: Introduction to Data Warehousing (2 hours)
   2. This module introduces data warehousing concepts and use cases. Topics include the purpose and benefits of a data warehouse (a central analytical repository), the differences between OLTP and OLAP systems, and the role of the warehouse in BI. Learners will study key terminology (facts, dimensions, OLAP vs OLTP, data marts, schemas) and understand the evolution of DW (from early warehouse architectures to modern cloud-based solutions). By the end of this module, participants can explain why organizations build warehouses and how they support reporting, analytics dashboards and data science applications.
   3. Topics Covered: Definition and business value of DW; components (data sources, staging area, DW, presentation layer); typical architectures and users (data analysts, BI developers).
   4. Key Concepts: Central repository for analytics; ETL as the means to populate the warehouse; overview of common DW use cases (sales reporting, trend analysis).
   5. Module 2: Data Warehouse Architecture & Design (4 hours)
   6. This module dives into design strategies and system architectures for data warehouses. Participants learn about top-down (Inmon) vs bottom-up (Kimball) vs hybrid design approaches. We cover multi-tier architectures (single-tier, two-tier, three-tier) and data flow layers (operational source systems → staging/ETL layer → integrated DW → data marts/OLAP cubes). Learners examine key architectural considerations: choosing hardware/storage, handling historical/time-variant data, and designing for scalability. Data Mart patterns (dependent vs independent) and the use of data warehouse appliances or cloud services are discussed. Hands-on examples illustrate architecture diagrams and tiered deployments.
   7. Topics Covered: DW design methodologies (Top-Down vs Bottom-Up); multi-tier DW architectures; ETL staging and data integration layer; data marts and OLAP cubes; hybrid (lambda) architectures.
   8. Key Concepts: How to choose an architecture based on business needs; trade-offs of normalization vs denormalization; importance of metadata and master data management in design.
   9. Module 3: Data Modeling – Star and Snowflake Schemas (4 hours)
   10. Learners explore dimensional modeling techniques, focusing on star and snowflake We cover fact tables and dimension tables, grain definition, and common dimension types (conformed, junk, slowly changing dimensions). The star schema, which has one central fact table linked to denormalized dimensions, is emphasized for its simplicity and fast query performance. In contrast, the snowflake schema adds normalization to dimensions to save storage at the cost of more joins. Participants will see how to design a star schema (facts, measures, degenerate dimensions) and when to snowflake (split dimensions into multiple related tables). Use cases illustrate each schema’s trade-offs.
   11. Topics Covered: Fact vs dimension tables; grain, keys and measures; star schema features (denormalized dims) and pros for OLAP; snowflake schema (normalized dims) and pros/cons; slowly changing dimension types (Type 1, 2, 3).
   12. Key Concepts: How schema design affects query speed and storage; role of surrogate keys; examples of star/schema diagrams for sales or inventory data.
   13. Module 4: ETL Processes (5 hours)
   14. This module covers the Extract-Transform-Load (ETL) pipeline that feeds the warehouse. We define ETL as the three-stage process of extracting data from source systems, transforming it (cleaning, conforming, validating), and loading it into the warehouse. Participants learn common extraction techniques (full load, incremental, change data capture) and tools (e.g. Informatica PowerCenter, SSIS). Transformation topics include data cleansing (deduplication, standardization), data mapping (joining disparate sources), and applying business rules or aggregations. We also discuss load strategies (bulk vs incremental loads, use of staging tables) and best practices (logging, error handling). Lab exercises involve building a basic ETL workflow in a tool.
   15. Topics Covered: ETL definitions and phases; source system connectivity; transformation logic (lookups, derived columns, filtering); loading strategies (batch vs trickle).
   16. Key Concepts: Ensuring data quality in ETL; ETL scheduling and orchestration; difference between ETL and ELT (modern pipelines that load then transform).
   17. Module 5: Cloud Data Warehouse Platforms (Snowflake & Amazon Redshift) (4 hours)
   18. Participants explore leading cloud data warehouse platforms. We study Snowflake – a fully managed, cloud-native DW that separates compute and storage for elastic scaling. Topics include Snowflake’s architecture, how to create databases/warehouses, and basic SQL operations (loading semi-structured JSON). We practice loading and querying data in Snowflake, learning how to size virtual warehouses for performance and how Snowflake’s features (cloning, time-travel) aid development. We then cover Amazon Redshift – Amazon’s petabyte-scale cloud DW. Key topics are Redshift clusters, node types, distribution styles and sort keys, and spectrum (querying S3 data). We discuss Redshift performance (columnar storage, compression), integration with AWS ETL tools (Glue), and typical use cases.
   19. Topics Covered: Snowflake deployment and data loading; Redshift cluster setup and querying; differences between cloud DWs (scalability, concurrency); comparing cloud vs on-prem solutions.
   20. Key Concepts: How cloud DWs simplify hardware management; cost/benefit of pay-as-you-go; cloud-specific features (auto-scaling, data sharing).
   21. Module 6: On-Premise DW & ETL Tools (Microsoft SQL Server & Informatica) (4 hours)
   22. This module introduces traditional on-premises data warehousing tools and ETL platforms. We review Microsoft SQL Server’s DW capabilities (including SQL Server Integration Services for ETL, SQL Server Analysis Services for cubes) and practice building a small dimensional schema in SQL Server. Next, we cover Informatica PowerCenter, a leading ETL/data integration suite. Topics include Informatica’s architecture (Repository Service, Integration Service), designing mappings, and managing workflows. Learners will map sample data through Informatica: extracting from a source, applying transformations, and loading into a SQL target. We emphasize best practices for on-prem tools and when to choose on-prem vs cloud.
   23. Topics Covered: SQL Server DW features (fact/dim creation, SSIS basics); Informatica PowerCenter fundamentals; connecting to various data sources; data flow design in Informatica.
   24. Key Concepts: Comparing on-prem vs cloud ETL; scenario discussion (e.g. existing SQL infrastructure), licensing and scalability considerations.
   25. Module 7: Business Intelligence & Reporting (4 hours)
   26. In this module, participants learn how data warehouses support BI and data visualization. We define Business Intelligence as processes and tools that turn data into reports and dashboards. Common BI tools (Tableau, Power BI, Looker) are introduced and connected to the warehouse. Key topics include designing effective dashboards, visualization principles (charts, KPIs), and self-service analytics. Learners practice building a simple dashboard from warehouse data (e.g. sales trends over time). We also cover ad-hoc querying and report generation. The goal is to illustrate how a well-designed warehouse enables fast, reliable reporting to decision-makers.
   27. Topics Covered: Overview of BI tools and their connection to DW; crafting dashboards and reports; interactive vs static reporting; OLAP cubes and slice-and-dice analysis.
   28. Key Concepts: The role of the warehouse as the single source for BI; data visualization best practices; real-time versus batch refresh for dashboards.
   29. Module 8: Data Governance & Quality (3 hours)
   30. This module covers data governance and quality management within a warehouse context. Learners study governance frameworks that ensure data in the warehouse is accurate, secure and compliant. Topics include data governance policies, data stewardship roles, metadata management, and regulatory compliance (e.g. GDPR, HIPAA). We emphasize that good governance is critical for trustworthy analytics. Data quality techniques are discussed: profiling data to find anomalies, validation rules to enforce standards, and cleansing procedures to fix errors. For example, students learn how to implement validation checks (e.g. referential integrity, valid ranges) and deduplication in ETL. Hands-on exercises involve profiling a sample dataset and defining rules to improve its quality.
   31. Topics Covered: Importance of DW governance (data lineage, access controls); data quality processes (profiling, cleansing); implementing data validation in pipelines; role of a data steward.
   32. Key Concepts: Building trust in data through accuracy and consistency; balancing accessibility with privacy/security; continuous monitoring of data quality.
   33. Module 9: Performance Optimization & Tuning (3 hours)
   34. Focusing on tuning the data warehouse for speed and scalability, this module teaches techniques to optimize query and load performance. Key topics include indexing strategies (clustered, non-clustered, bitmap) and partitioning large tables to reduce query I/O. Learners will practice designing indexes and partition schemes in SQL Server and review how Snowflake or Redshift handle distribution. We cover other optimizations: using materialized views or pre-aggregations for complex queries, query rewriting, and workload management (concurrency scaling, caching). Hardware/storage considerations are also discussed (e.g. SSD vs HDD, compression). The session includes tuning exercises: given a slow-running query, identify bottlenecks and apply fixes.
   35. Topics Covered: SQL query tuning (joins, subqueries); use of indexing and partitioning to speed retrieval; caching and parallel query execution; monitoring tools for performance.
   36. Key Concepts: Ensuring the warehouse serves analytics with low latency; how storage format and compute resources impact throughput; trade-offs in tuning (e.g. more indexes vs slower writes).
   37. Module 10: Case Studies & Hands-On Labs (4 hours)
   38. This practical module integrates previous lessons through guided hands-on labs and case studies. Participants work on scenarios such as designing a retail sales data mart or a marketing analytics warehouse. For each case, they draft a schema, populate it via an ETL pipeline, and create reports. For example, a case study may involve building a star schema for a fictional company’s sales data, writing ETL transformations, and using a BI tool to analyze it. These exercises reinforce learning: students apply ETL tools (Informatica/SSIS), write DW SQL queries, and follow best practices in governance. Mentors provide feedback, ensuring key skills (data modeling, ETL logic, query tuning) are mastered.
   39. Labs and Cases: End-to-end mini-projects (e.g. “Build a Sales Data Warehouse” case with star schema design, ETL from CSVs, and a Tableau dashboard).
   40. Key Activities: Debugging ETL pipelines; optimizing a reporting query; implementing a slowly changing dimension; documenting the solution design.
   41. Module 11: Capstone Project (3 hours)
   42. In the final capstone project, learners apply all skills to a comprehensive data warehousing challenge. Working individually or in teams, they design and implement a working data warehouse for a given real-world scenario (e.g. e-commerce analytics, hospital patient data, etc.). Tasks include: defining the schema (star/snowflake), developing the ETL workflow, loading data into Snowflake or Redshift, and creating sample reports. Each team delivers a presentation of their end-to-end solution along with documentation of design decisions. The capstone emphasizes problem-solving with guidance, preparing students to tackle data warehousing projects in a professional setting.
   43. Project Elements: Scenario analysis, schema design, ETL pipeline development, BI reporting.
   44. Deliverables: Functional DW implementation (on chosen platform), SQL scripts/ETL mappings, dashboards, and a summary report of learnings and optimizations.

Key Features

  Comprehensive Curriculum: 10+ modules covering DW fundamentals, ETL, modeling, BI integration, governance, optimization, and case studies.

  Hands-On Labs & Case Studies: Practical exercises and real-world scenarios reinforce each topic (e.g. building an ETL pipeline, designing star schemas, etc.).

  Tool-Specific Sessions: Guided tutorials on popular platforms – Snowflake, Amazon Redshift, Microsoft SQL Server, and Informatica – including setup and best practices.

  Capstone Project: A final project applies all skills to design and implement a working data warehouse solution, with presentation and documentation.

  Expert Instruction: Industry-experienced instructors lead interactive sessions, with Q&A and review for each module.

  Outcome-Oriented: Focus on building job-relevant skills (ETL, data modeling, query tuning, BI reporting) and providing career support and certification guidance.