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