Data Integration & Warehousing Techniques Training Course

This course equips participants with the knowledge and practical skills to design, implement, and manage data integration and warehousing solutions. It emphasizes the processes of data extraction, transformation, and loading (ETL), as well as modern approaches to data lakes, cloud warehouses, and real-time integration. Participants will learn how to build scalable, reliable, and efficient data infrastructures to support analytics, business intelligence, and enterprise decision-making.

Target Groups

• Data engineers and architects
• Database administrators and IT specialists
• Business intelligence and analytics professionals
• Software developers working on data systems
• Project managers overseeing data infrastructure projects
• Students pursuing data engineering, IT, or computer science studies

Course Objectives

By the end of this course, participants will be able to:

• Understand the role of data integration and warehousing in business intelligence.
• Design and implement ETL pipelines for structured and unstructured data.
• Manage and optimize data warehouses for performance and scalability.
• Apply data modeling techniques for warehouse design.
• Utilize cloud-based warehousing platforms effectively.
• Ensure data quality, consistency, and governance in integration processes.
• Integrate real-time and streaming data into warehouse systems.
• Differentiate between data warehouses, lakes, and lakehouses.
• Apply security and compliance measures in data integration projects.
• Deliver actionable insights through well-structured data systems.

Course Modules

Module 1: Introduction to Data Integration & Warehousing

• Importance of data warehouses in enterprise systems
• Key concepts of integration, ETL, and data pipelines
• Data lakes vs. data warehouses vs. lakehouses
• Case studies in enterprise data management

Module 2: ETL Processes & Tools

• Fundamentals of extraction, transformation, and loading
• Designing ETL workflows for multiple data sources
• Tools for ETL (Informatica, Talend, Apache NiFi)
• Ensuring efficiency and reliability in ETL pipelines

Module 3: Data Modeling for Warehousing

• Star, snowflake, and galaxy schemas
• Fact and dimension tables
• Best practices in data modeling for analytics
• Handling slowly changing dimensions

Module 4: Building & Managing Data Warehouses

• Architecture of traditional and modern warehouses
• On-premises vs. cloud-based solutions
• Partitioning, indexing, and query optimization
• Maintenance and performance tuning

Module 5: Cloud Data Warehousing Platforms

• AWS Redshift, Google BigQuery, Azure Synapse
• Advantages and challenges of cloud adoption
• Cost optimization strategies for cloud warehouses
• Hybrid and multi-cloud environments

Module 6: Real-Time Data Integration

• Batch vs. real-time integration approaches
• Streaming data with Apache Kafka and Spark Streaming
• Event-driven architectures for real-time analytics
• Use cases in operations and IoT data

Module 7: Data Quality & Governance

• Ensuring accuracy, consistency, and completeness of data
• Master data management (MDM) principles
• Metadata management and lineage tracking
• Governance frameworks for enterprise systems

Module 8: Security & Compliance in Data Warehousing

• Data privacy laws and compliance requirements (GDPR, HIPAA)
• Encryption, authentication, and access control
• Risk management in data integration projects
• Building secure data warehouse architectures

Module 9: Business Intelligence & Analytics Enablement

• Role of data warehouses in BI and analytics
• Integrating warehouses with BI tools (Power BI, Tableau, Qlik)
• Performance dashboards and reporting
• Translating data into strategic insights

Module 10: Capstone Project & Case Studies

• Real-world data warehousing and integration projects
• Group project: building a mini data warehouse with ETL pipeline
• Presentation of insights enabled by warehouse design
• Future trends in data warehousing and integration technologies