Data Integration Architectures
Data Engineer⏱ 20 Hours Estimated~3 min read
Mapped Subtopics & Architecture
- Processing paradigms (Batch Processing vs. Real-Time Event-Driven Streaming)
- Modern Ingestion Frameworks (Airbyte, Fivetran, Meltano)
Data Integration Architectures
Discipline: Data Engineer | Module: Module 3: ETL/ELT Pipeline Engineering, Streaming & Orchestration | Estimated Study Time: 20 Hours
Welcome to Data Integration Architectures. This topic delivers foundational and advanced concepts designed for production engineering and real-world workflows.
Key Learning Objectives
- Processing paradigms (Batch Processing vs. Real-Time Event-Driven Streaming)
- Modern Ingestion Frameworks (Airbyte, Fivetran, Meltano)
Detailed Curriculum Breakdown
Processing paradigms (Batch Processing vs. Real-Time Event-Driven Streaming)
Explore the fundamental principles, real-world patterns, and best practices for Processing paradigms (Batch Processing vs. Real-Time Event-Driven Streaming). Practice hands-on implementations to master these concepts.
// Code Example: Processing paradigms (Batch Processing vs. Real-Time Event-Driven Streaming)
// Implement verified patterns for production use
console.log("Mastering Processing paradigms (Batch Processing vs. Real-Time Event-Driven Streaming)");
Modern Ingestion Frameworks (Airbyte, Fivetran, Meltano)
Explore the fundamental principles, real-world patterns, and best practices for Modern Ingestion Frameworks (Airbyte, Fivetran, Meltano). Practice hands-on implementations to master these concepts.
// Code Example: Modern Ingestion Frameworks (Airbyte, Fivetran, Meltano)
// Implement verified patterns for production use
console.log("Mastering Modern Ingestion Frameworks (Airbyte, Fivetran, Meltano)");
Practical Application & Exercises
- Architecture Review: Evaluate how Data Integration Architectures integrates with upstream and downstream systems.
- Implementation Challenge: Build a functional prototype demonstrating each of the subtopics.
- Validation & Testing: Verify performance and error handling under edge-case scenarios.
Summary Checklist
- Studied foundational architecture for Data Integration Architectures
- Completed practical coding challenge
- Validated edge cases and error handling routines
