Training on Data Integration Techniques: Mastering the Art of Seamless Data Integration

Module 1: Introduction to Data Integration

• Core concepts and importance of data integration
• Types of data sources, structures, and formats
• Common integration challenges and practical solutions
• Case Study: How a retail company unified siloed systems to improve analytics
• Practical Exercise: Identify integration gaps in a sample multi-source dataset

Module 2: Data Profiling and Data Quality

• Techniques for profiling structured and unstructured data
• Assessing data accuracy, completeness, consistency, and validity
• Data cleansing, validation, and standardization procedures
• Case Study: Improving data quality in a financial institution
• Practical Exercise: Perform profiling and quality checks on provided datasets

Module 3: ETL/ELT Processes

• Differences between ETL and ELT and when to use each
• ETL phases: Extract, Transform, Load
• ELT phases: Extract, Load, Transform
• Transformation techniques for analytics and reporting
• Case Study: Migrating from traditional ETL to cloud-based ELT pipelines
• Practical Exercise: Design a simple ETL/ELT workflow

Module 4: Data Modeling for Integration

• Foundations of conceptual, logical, and physical data models
• Dimensional modeling (star & snowflake schemas)
• Data warehouse, data marts, and data lake architectures
• Case Study: Building an enterprise data model for a logistics company
• Practical Exercise: Create a basic dimensional model for reporting

Module 5: Data Integration Tools and Technologies

• Overview of leading tools (Informatica, Talend, SSIS, Pentaho, Airflow, etc.)
• Cloud vs on-premises integration solutions
• Criteria for selecting the right tool
• Case Study: Choosing a scalable integration tool for a growing enterprise
• Practical Exercise: Compare tool capabilities in a selection matrix

Module 6: Data Extraction Techniques

• Extracting data from relational databases
• File-based extraction (CSV, XML, JSON, Parquet)
• API-based extraction and automation
• Change Data Capture (CDC) for real-time integration
• Case Study: Implementing CDC to support real-time reporting
• Practical Exercise: Extract and prepare data from multiple source formats

Module 7: Data Transformation and Enrichment

• Standardization, cleaning, and normalization
• Data aggregation and restructuring
• Data enrichment using external sources
• Case Study: Transforming customer data for 360° visibility
• Practical Exercise: Implement transformations for a multi-table dataset

Module 8: Data Loading and Deployment

• Loading strategies for data warehouses and lakes
• Full loads, incremental loads, and CDC-based loads
• Load optimization techniques
• Case Study: Scaling data load processes for a high-volume enterprise
• Practical Exercise: Configure an incremental load process

Module 9: Data Integration Testing and Monitoring

• Data validation, reconciliation, and integrity tests
• Performance and stress testing
• Monitoring pipelines for reliability and accuracy
• Case Study: Building a monitoring dashboard for ETL operations
• Practical Exercise: Conduct validation tests on a sample pipeline

Module 10: Data Integration Best Practices & Case Studies

• Governance, metadata management, and documentation standards
• Security, privacy, and compliance considerations
• Real-world case studies of successful data integration projects
• Case Study: Enterprise-wide data transformation using cloud integration
• Practical Exercise: Develop a mini integration strategy for a provided scenario