Data Integration Fundamentals Course Syllabus

This course provides a practical, end-to-end introduction to data integration fundamentals, designed for beginners. Over approximately 6 hours of content, you'll learn core concepts, tools, and best practices for building reliable data pipelines. Each module combines theory with hands-on techniques, covering extraction, transformation, loading, orchestration, and data quality. The course concludes with real-world troubleshooting and performance tuning strategies, preparing you for entry-level data engineering roles.

Module 1: Introduction to Data Integration

Estimated time: 0.5 hours

• Overview of data integration use cases and architecture styles
• Key terminology: ETL, ELT, data lake, data warehouse
• Distinguishing streaming vs. batch integration
• Understanding the role of data pipelines in analytics

Module 2: Data Extraction Techniques

Estimated time: 0.75 hours

• Connecting to relational databases and flat files
• Integrating with REST APIs
• Full-load vs. incremental extraction strategies
• Introduction to change data capture (CDC)

Module 3: Data Transformation & Cleansing

Estimated time: 1 hour

• Performing joins, aggregations, and lookups during transit
• Handling missing values and duplicate records
• Data normalization techniques
• Ensuring consistency across transformation steps

Module 4: Loading & Target System Design

Estimated time: 0.75 hours

• Implementing bulk inserts and upserts
• Slowly Changing Dimension (SCD) techniques
• Designing schemas for OLAP and reporting
• Best practices for target data storage

Module 5: Integration Tools & Platforms

Estimated time: 1 hour

• Overview of open-source tools like Apache NiFi and Airflow
• Comparing commercial ETL platforms
• Writing custom scripts vs. using graphical pipeline tools
• Selecting the right tool for integration needs

Module 6: Job Orchestration & Scheduling

Estimated time: 0.75 hours

• Workflow scheduling and dependency management
• Error handling in pipeline execution
• Monitoring with logging and dashboards
• SLA tracking and alerting mechanisms

Module 7: Data Quality & Governance

Estimated time: 0.75 hours

• Implementing data validation rules
• Auditing and data lineage tracking
• Metadata management fundamentals
• Documentation best practices

Module 8: Performance Tuning & Troubleshooting

Estimated time: 0.5 hours

• Optimizing query performance and resource use
• Leveraging parallelism in data pipelines
• Debugging common integration failures
• Recovery strategies for pipeline resilience

Prerequisites

• Basic understanding of SQL
• Familiarity with databases and file formats
• No prior ETL experience required

What You'll Be Able to Do After

• Explain core data integration patterns like ETL and ELT
• Design and implement end-to-end data pipelines
• Ensure data quality through cleansing and validation
• Orchestrate and monitor integration workflows
• Diagnose and fix common pipeline issues