AWS Data Engineering Fundamentals: ETL, Data Integration & Workflow Orchestration Explained | Part 2

Data is only valuable when it can be transformed into meaningful insights.

In this presentation, I explore the fundamentals of ETL (Extract, Transform, Load) Pipelines, Data Integration, and Workflow Orchestration in AWS as part of my preparation for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification.

📌 Topics Covered:

✅ What is ETL and why it matters
✅ Extract, Transform, and Load phases explained
✅ Data quality and its impact on Machine Learning models
✅ Data integration from multiple enterprise sources
✅ AWS Glue for serverless ETL automation
✅ AWS Lambda for event-driven processing
✅ Amazon EventBridge for workflow automation
✅ Amazon Managed Workflows for Apache Airflow (MWAA)
✅ End-to-End AWS ETL Architecture

Modern machine learning systems depend heavily on reliable and well-structured data. Understanding ETL pipelines and orchestration services is essential for Data Engineers, Cloud Engineers, Machine Learning Engineers, and anyone working with large-scale data processing.

This presentation demonstrates how AWS services work together to automate data movement, improve scalability, and prepare high-quality datasets for analytics and machine learning workloads.

#AWS #MachineLearning #DataEngineering #ETL #AWSGlue #Lambda #EventBridge #MWAA #CloudComputing #BigData

Видео AWS Data Engineering Fundamentals: ETL, Data Integration & Workflow Orchestration Explained | Part 2 канала G Tech