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Data Integration Architectures

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

  1. Processing paradigms (Batch Processing vs. Real-Time Event-Driven Streaming)
  2. 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

  1. Architecture Review: Evaluate how Data Integration Architectures integrates with upstream and downstream systems.
  2. Implementation Challenge: Build a functional prototype demonstrating each of the subtopics.
  3. Validation & Testing: Verify performance and error handling under edge-case scenarios.

Summary Checklist