1. Course Syllabus

   1. Module 1: Introduction to Data Warehousing (Duration: 4 hours)
   2. Concepts Covered: Definitions of OLTP vs. OLAP, purpose and benefits of a data warehouse, EDW vs. data marts, common architectures (single-/two-/three-tier). Learners discuss how EDWs solve “big data” challenges by integrating disparate data for analytics.
      Tools & Technologies: Overview of RDBMS and warehouse platforms (Oracle, Teradata, Snowflake, Redshift), and basic BI/OLAP tools. Introduction to database schemas and metadata.
      Learning Outcomes: Understand why enterprises build warehouses, the typical EDW components (staging, warehouse, metadata repository, BI front end), and the role EDWs play in decision-making. By the end, participants can outline a high-level EDW architecture and explain its importance for business intelligence.
   3. Module 2: Data Warehouse Design & Dimensional Modeling (Duration: 4 hours)
   4. Concepts Covered: Data modeling techniques for warehouses. This includes Kimball (star/snowflake schemas) vs. Inmon approaches, fact and dimension tables, slowly changing dimensions, and normalization trade-offs. Learners design schemas (star and snowflake) to support typical analytical queries.
      Tools & Technologies: Hands-on practice using a data modeling tool or SQL database. Examples of schema diagrams and how to implement them in Oracle or Teradata. Discussion of standards like SSIS templates or Oracle Data Modeling tools.
      Learning Outcomes: Ability to translate business requirements into a dimensional schema. Learners will model a sample use case (e.g. sales or patient data) into fact and dimension tables, and explain how this design supports fast OLAP queries. This module ensures participants can plan an EDW data model before implementation.
   5. Module 3: ETL and Data Integration Fundamentals (Duration: 4 hours)
   6. Concepts Covered: ETL (Extract–Transform–Load) vs. ELT processes. Learners study data ingestion workflows: extraction from sources (databases, flat files, logs), transformation (cleaning, join, business rules), and loading into the warehouse. Concepts like batch vs. real-time loads, data quality, and metadata management are discussed.
      Tools & Technologies: Introduction to Informatica PowerCenter (or similar ETL tool). Learners see how an ETL tool connects to sources, designs mappings, and executes jobs. Basic Informatica components (Repository, Designer, Workflow Manager) are covered.
      Learning Outcomes: Students can explain ETL architecture and build a simple ETL pipeline: extract from a transactional DB, transform data (e.g., type conversions, lookups), and load it into a staging table. They learn why correct sequence (ETL vs. ELT) matters and how to ensure data integrity. Learners will also sketch a metadata-driven ETL design, reflecting industry best practices
   7. Module 4: Informatica PowerCenter for Data Warehousing (Duration: 4 hours)
   8. Concepts Covered: Deep dive into a leading ETL tool. Topics include Informatica architecture, repository vs. integration service, and workflow scheduling. Learners cover PowerCenter transformations (Aggregator, Sorter, Lookup, Router, etc.) and how to implement CDC (Change Data Capture). The role of version control and collaboration in ETL development is introduced.
      Tools & Technologies: Hands-on lab with Informatica PowerCenter: configuring a repository, creating source/target definitions, and designing mapping to load data into a sample warehouse schema. Students run workflows and use tools like Informatica Workflow Monitor.
      Learning Outcomes: By practicing actual data integration tasks, learners build proficiency in a major ETL platform. They will complete an end-to-end ETL project in Informatica (e.g. loading a sample sales database) and understand how to optimize and troubleshoot workflows. This module prepares them for Informatica’s Certified Professional exam by covering core concepts and hands-on skills .
   9. Module 5: Oracle Data Warehousing (Duration: 4 hours)
   10. Concepts Covered: Oracle-specific warehouse features. This includes Oracle’s analytic SQL extensions (e.g. analytic functions, Materialized Views), and high-performance features (parallel query, partitioning, Bitmap indexes). Learners also see Oracle Warehouse Builder or Autonomous Data Warehouse Cloud basics.
       Tools & Technologies: Use Oracle Database (on-premise or Cloud) for demonstrations. Topics like tablespaces, datafile placement, and workspace management are covered. BI tools like Oracle Analytics or SQL Developer Data Modeler may be introduced.
       Learning Outcomes: Participants will understand how Oracle’s technologies support a DW – for example, using materialized views for fast aggregations, and how to tune a warehouse for query performance. By hands-on lab, learners will implement a small star schema in Oracle, load data (using SQL*Loader or ETL), and write analytical queries (using window functions). The outcome is familiarity with Oracle’s DW best practices, aligning with Oracle’s data warehousing certification objectives .
   11. Module 6: Teradata Data Warehousing (Duration: 4 hours)
   12. Concepts Covered: Teradata’s architecture and strengths. Key ideas include Teradata’s Massively Parallel Processing (MPP), shared-nothing design, and row distribution (hashing). Students learn about parsing engine vs. AMP nodes, the BYNET interconnect, and how data is spread across AMPs. Topics include Teradata’s indexing options (primary, secondary, join indexes) and load/backup utilities.
       Tools & Technologies: Teradata Vantage (or an educational sandbox). Learners practice SQL on Teradata and see how to distribute data by choosing primary indexes. Basic Teradata tools (e.g. TPT - Teradata Parallel Transporter) for loading data are shown.
       Learning Outcomes: Learners gain hands-on experience querying and loading data in Teradata. They will load a dataset into a Teradata table (observing how rows are hashed and distributed) and run a parallel query to see performance. Outcomes include understanding why Teradata excels at very large data volumes and complex analytical workloads. Graduates of this module can leverage Teradata’s parallelism to build enterprise-scale data marts.
   13. Module 7: Snowflake Cloud Data Warehousing (Duration: 4 hours)
   14. Concepts Covered: Snowflake’s cloud-native architecture. Core concepts include separate storage and compute layers (elastic scaling) and multi-cluster shared data. Learners study micro-partitioning, automatic query optimization, and features like zero-copy cloning, time travel, and data sharing. The instructor also covers Snowflake’s SQL and how it implements star schemas.
       Tools & Technologies: Snowflake Data Cloud environment. Students run queries in Snowflake’s Worksheets, configure virtual warehouses (compute clusters), and experiment with scaling compute up/down. They use SnowSQL or the web GUI for loading data from cloud storage (S3 or Azure) and create schemas.
       Learning Outcomes: Students will load sample data into Snowflake and perform queries to see how compute scales. They learn that Snowflake decouples storage and compute for flexibility and cost-effectiveness. By the end, participants can articulate Snowflake’s three-layer architecture and use its SQL console to create tables, run analytic queries, and leverage features like zero-copy cloning. (This prepares learners for Snowflake’s SnowPro Core certification topics.)
   15. Module 8: AWS Redshift Data Warehousing (Duration: 4 hours)
   16. Concepts Covered: AWS Redshift architecture and cloud data warehouse concepts. Topics include Redshift clusters (leader node, compute nodes, slices) and columnar storage. Learners cover data distribution styles (KEY, EVEN, ALL) and how Redshift Spectrum enables querying data in S3. Security and scaling features (RA3 nodes, concurrency scaling) are also introduced.
       Tools & Technologies: Amazon Redshift on AWS. Students create a Redshift cluster, load data via COPY from S3, and run queries. The module may also cover Redshift Spectrum by defining an external schema on data in S3 and querying it. Use of AWS Glue or Kinesis for continuous loading is mentioned.
       Learning Outcomes: After this module, learners will understand Redshift’s MPP design and how to optimize it. They will practice loading a large dataset into Redshift and see how distribution keys affect query speed. The lab includes issuing a Redshift Spectrum query on external data, illustrating modern data lake integration. Learners can then compare Redshift to Snowflake and on-prem databases, noting trade-offs in cloud scalability and performance
   17. Module 9: Industry Use Case Workshops (Finance, Healthcare, Retail) (Duration: 4 hours)
   18. Concepts Covered: Application of EDW in key industries. In finance, learners examine a DW built for financial reporting: consolidating banking transactions, portfolio data, and risk metrics. In healthcare, they study a warehouse that integrates EHR, claims, and patient data to improve outcomes. In retail, a case focuses on a DW aggregating sales, inventory, and customer behavior for demand forecasting. Each mini-project demonstrates how dimensional modeling and ETL adapt to domain needs.
       Tools & Technologies: All of the above platforms may be used. For example, students might build a sample financial data mart in Oracle or Snowflake, a healthcare analytics view in Redshift, or a retail sales DW in Teradata. Dashboards or reports (using SQL or BI tools) are generated from these DWs to answer business questions.
       Learning Outcomes: By working through real scenarios, learners see how EDW principles apply outside the lab. They learn to define subject-area data marts (e.g. a patient analytics mart vs. a sales mart) and write queries/report scripts to analyze KPIs. This module reinforces skills like model design and ETL by solving concrete problems (e.g. “what is the quarterly revenue by product?” or “how did treatment outcomes change over time?”). It also highlights industry regulations: finance data warehouses must support auditing/compliance, and healthcare DWs must enforce security and privacy (HIPAA) as part of their design.
   19. Module 10: Capstone Project and Course Review (Duration: 4 hours)
   20. Concepts Covered: Integration of all learned concepts into a final project. Typically, learners work in teams or individually to architect and build an end-to-end mini data warehouse on a chosen platform (e.g. Snowflake or Redshift). They apply requirements gathering, modeling, ETL, and reporting in a compressed timeframe. The instructor reviews advanced topics such as real-time analytics or emerging trends (e.g. data lakes, Hadoop integration) and answers outstanding questions.
       Tools & Technologies: Any of the course’s tools. For the capstone, a cloud DW is recommended to provide resources on demand. Students will use SQL, ETL jobs (Informatica or AWS Glue), and BI queries. The review may also include certification exam tips (e.g. key AWS DAT exam topics, or sample Snowflake SnowPro questions).
       Learning Outcomes: Learners deliver a functioning DW prototype and present findings, demonstrating they can implement an enterprise-scale solution. They leave the program capable of starting a real-world EDW project and with clear next steps toward any certification (e.g. Oracle OCP, Snowflake SnowPro, AWS Data Analytics, Informatica Developer). Overall, the capstone synthesizes the course into a polished, practical experience.

Key Features

  Hands-On Labs & Projects: The program emphasizes “learning by doing.” Each module includes practical labs where learners design schemas, load data, and run analytics in real environments. Research shows hands-on labs greatly boost retention and proficiency by guiding students through realistic exercises and projects.

  Industry Use Cases: Instruction includes real-world scenarios in finance, healthcare, and retail. For example, learners might build a healthcare DW that integrates patient and claims data to improve clinical decisions, or a financial DW that unifies transaction data for regulatory reporting and forecasting. These case studies illustrate how EDW solutions drive value in different sectors.

  Tool & Platform Diversity: Modules cover major EDW technologies: relational warehouses (Oracle, Teradata) and cloud warehouses (Snowflake, AWS Redshift), plus leading ETL (Informatica). Learners gain experience with each platform’s architecture and best practices, reflecting what large enterprises commonly use. This multi-platform approach ensures skills portability across jobs.

  Certification Alignment: Content maps to industry certification paths. For example, Oracle Database and Data Warehousing exams, Snowflake’s SnowPro certifications, AWS Data Analytics (including Redshift), and Informatica Professional credentials. The training highlights key competencies needed for these certs, helping motivated learners prepare for credential exams.

  Comprehensive Curriculum: The course progresses from fundamentals to advanced topics. It starts with core DW concepts and data modeling, then moves through ETL design and specific platforms. The syllabus blends theory and practice in short modules (3–4 hours each), making it suitable for beginners while still covering advanced features. Each section ends with tangible outcomes (e.g. a working DW prototype, an ETL workflow) so learners continuously apply their knowledge.