Build practical data engineering and analytics skills with Open Source Data Warehousing Training, a specialized course designed for data professionals and IT teams who want to understand how to design, develop, and manage modern data warehouse solutions using open-source technologies. The training introduces the essential concepts, processes, and tools used to transform raw data into organized and meaningful information for reporting and decision-making.
Open Source Data Warehousing Overview
This Open Source Data Warehousing Training Course provides participants with practical knowledge and hands-on experience in designing, building, managing, and optimizing enterprise data warehouse solutions using open source technologies.
The course covers core concepts of data warehousing, dimensional modeling, ETL/ELT development, data integration, reporting, analytics, and performance optimization using widely adopted open source tools and platforms such as PostgreSQL, MySQL, Apache Airflow, Pentaho Data Integration (Kettle), Talend Open Studio, Metabase, Apache Superset, and Docker-based environments.
Participants will learn how to consolidate and transform data from multiple systems into centralized repositories for business intelligence, analytics, and decision-making while applying industry best practices and scalable architecture approaches.
The training includes lectures, demonstrations, guided labs, hands-on workshops, and real-world implementation exercises using open source environments.
Duration 5 Days – 35 hrs.
Objectives
- Understand data warehousing concepts and architecture
- Differentiate transactional and analytical systems
- Design dimensional models using fact and dimension tables
- Build data warehouse schemas using open source databases
- Develop ETL/ELT workflows using open source tools
- Perform data extraction, transformation, and loading processes
- Implement data quality and cleansing techniques
- Create analytical reports and dashboards
- Optimize warehouse performance and queries
- Understand modern open source analytics ecosystems
- Deploy and manage open source data warehousing environments
Target Audience
- Data Analysts
- Data Engineers
- Database Administrators
- Business Intelligence Developers
- ETL Developers
- Software Developers
- Reporting Specialists
- IT Professionals
- System Analysts
- Technical Project Managers
- Professionals transitioning into analytics and data engineering roles
Prerequisites
- Basic knowledge of SQL and relational databases
- Basic understanding of data structures and reporting concepts
- Familiarity with Linux or command-line basics is an advantage
- Prior experience with databases or development is beneficial but not mandatory
Course Outline
Module 1: Introduction to Data Warehousing
- Overview of Data Warehousing
- Benefits and Business Use Cases
- Data Warehouse Concepts and Terminologies
- OLTP vs OLAP Systems
- Enterprise Analytics Architecture
- Components of a Data Warehouse
- Traditional vs Modern Data Warehousing
- Open Source Data Warehouse Ecosystem Overview
Hands-On
- Exploring sample warehouse environments
- Installing open source tools overview
Module 2: Open Source Data Warehouse Architecture
- Data Warehouse Architecture Components
- Enterprise Data Warehouse (EDW)
- Operational Data Store (ODS)
- Data Marts
- Staging Areas
- Metadata Management
- Batch vs Real-Time Data Processing
- Cloud vs On-Premise Open Source Solutions
Open Source Technologies Covered
- PostgreSQL
- MySQL/MariaDB
- Docker
- pgAdmin
- DBeaver
Hands-On
- Setting up PostgreSQL Data Warehouse Environment
- Configuring Docker-based warehouse lab
Module 3: Dimensional Modeling and Schema Design
- Introduction to Dimensional Modeling
- Fact Tables and Dimension Tables
- Star Schema Design
- Snowflake Schema Design
- Slowly Changing Dimensions (SCD)
- Granularity and Aggregation
- Surrogate Keys
- Data Modeling Best Practices
Hands-On
- Designing a retail sales warehouse schema
- Creating fact and dimension tables in PostgreSQL
Module 4: SQL for Data Warehousing
- SQL Fundamentals Review
- Advanced SQL Queries
- Joins and Aggregations
- Window Functions
- Common Table Expressions (CTE)
- Data Summarization
- Query Optimization Basics
- Materialized Views
Hands-On
- Building analytical SQL queries
- Generating reports from warehouse data
- Optimizing warehouse queries
Module 5: ETL/ELT Fundamentals Using Open Source Tools
- Introduction to ETL and ELT
- Data Extraction Techniques
- Data Transformation Methods
- Data Loading Strategies
- Incremental and Full Loads
- Data Validation and Cleansing
- ETL Error Handling
- Workflow Scheduling and Automation
Open Source ETL Tools Covered
- Pentaho Data Integration (Kettle)
- Talend Open Studio
- Apache Airflow (Introduction)
Hands-On
- Building ETL pipelines using Pentaho/Talend
- Extracting and transforming CSV/database data
- Loading data into PostgreSQL warehouse
Module 6: Data Quality and Governance
- Data Quality Fundamentals
- Data Profiling Techniques
- Handling Missing and Duplicate Data
- Master Data Management Concepts
- Metadata Management
- Data Governance Best Practices
- Data Security and Access Control
- Backup and Recovery Concepts
Hands-On
- Data cleansing exercises
- Implementing validation rules
Module 7: Business Intelligence and Reporting
- Introduction to Business Intelligence
- Reporting and Analytics Concepts
- KPIs and Metrics
- Dashboard Design Principles
- Self-Service Analytics
- Data Visualization Best Practices
Open Source BI Tools Covered
- Metabase
- Apache Superset
Hands-On
- Connecting BI tools to PostgreSQL
- Building dashboards and visual reports
- Creating KPIs and charts
Module 8: Performance Optimization and Administration
- Warehouse Performance Tuning
- Query Optimization
- Indexing Strategies
- Partitioning Concepts
- Storage Optimization
- Monitoring Warehouse Performance
- Capacity Planning
- Maintenance Best Practices
Hands-On
- Index tuning exercises
- PostgreSQL performance monitoring
- Query execution analysis
Module 9: Modern Open Source Data Warehousing
- Introduction to Data Lakes
- Data Warehouse vs Data Lake
- Lakehouse Concepts
- Big Data Integration
- Real-Time Data Processing Concepts
- Open Source Analytics Ecosystem
Open Source Platforms Overview
- Apache Hadoop
- Apache Spark
- Apache Kafka
- ClickHouse
- DuckDB
Hands-On
- Introduction to Spark SQL concepts
- Exploring modern analytical engines
Module 10: End-to-End Data Warehouse Project
- Designing a Complete Data Warehouse Solution
- Data Modeling Workshop
- ETL Workflow Development
- Data Integration Exercise
- Dashboard and Reporting Development
- Performance Optimization Review
- Deployment Best Practices
- Final Project Presentation
Capstone Hands-On Project
- Build a mini enterprise warehouse
- Create ETL pipelines
- Develop dashboards
- Generate analytical reports
- Apply optimization techniques
Suggested Open Source Software Stack
| Category | Open Source Software |
| Database | PostgreSQL / MariaDB |
| Database Client | DBeaver / pgAdmin |
| ETL Tool | Pentaho Data Integration / Talend Open Studio |
| Workflow Orchestration | Apache Airflow |
| BI & Dashboards | Metabase / Apache Superset |
| Containerization | Docker |
| Big Data (Optional) | Apache Spark / Hadoop |
