What will you in Data Integration Fundamentals Course
Understand core data integration concepts: ETL vs. ELT, data pipelines, and integration patterns
Work with common integration technologies and tools (e.g., SQL-based pipelines, APIs, message queues)
Design and implement robust extract, transform, load (ETL) workflows
Ensure data quality and consistency through validation, cleansing, and schema management
Monitor, schedule, and troubleshoot integration jobs for reliable data delivery
Program Overview
Module 1: Introduction to Data Integration
⏳ 30 minutes
Overview of data integration use cases and architecture styles
Key terminology: ETL, ELT, data lake, data warehouse, and streaming vs. batch
Module 2: Data Extraction Techniques
⏳ 45 minutes
Connecting to source systems: relational databases, flat files, REST APIs
Incremental vs. full-load strategies and change data capture basics
Module 3: Data Transformation & Cleansing
⏳ 1 hour
Applying joins, aggregations, and lookups in-transit
Handling missing values, duplicate records, and data normalization
Module 4: Loading & Target System Design
⏳ 45 minutes
Bulk inserts, upserts, and slowly changing dimension techniques
Designing target schemas for OLAP and reporting
Module 5: Integration Tools & Platforms
⏳ 1 hour
Overview of open-source (e.g., Apache NiFi, Airflow) and commercial ETL tools
Writing custom scripts vs. using graphical pipelines
Module 6: Job Orchestration & Scheduling
⏳ 45 minutes
Workflow scheduling, dependencies, and error handling
Monitoring and alerting with logging, dashboards, and SLA tracking
Module 7: Data Quality & Governance
⏳ 45 minutes
Implementing validation rules, auditing, and lineage tracking
Metadata management and documentation best practices
Module 8: Performance Tuning & Troubleshooting
⏳ 30 minutes
Optimizing resource utilization, parallelism, and query performance
Debugging common pipeline failures and recovery strategies
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Job Outlook
Data integration expertise is in high demand for roles such as Data Engineer, ETL Developer, and Integration Specialist
Applicable across industries building data warehouses, analytics platforms, and real-time dashboards
Provides a foundation for advanced work in big data frameworks (Spark, Kafka) and cloud integration services
Opens opportunities in roles focused on data quality, governance, and scalable pipeline design
Specification: Data Integration Fundamentals
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