Скачать или смотреть Build a Scalable AWS Data Pipeline with Amazon EMR, AWS Glue & Apache Airflow

🚀 Build a Scalable AWS Data Pipeline with Amazon EMR, AWS Glue & Apache Airflow

Learn how to build a robust, end-to-end data pipeline on AWS Cloud using Amazon EMR (Elastic MapReduce), AWS Glue, and Apache Airflow. This step-by-step tutorial will help you design, configure, and optimize a production-ready pipeline for big data processing, ETL, and workflow orchestration.

🔧 What You’ll Learn
What is a data pipeline and why it crucial for data engineering and analytics?

Amazon EMR: Distributed big data processing with Hadoop & Spark.

AWS Glue: Managed ETL service for data ingestion, transformation & cataloging.

Apache Airflow: Workflow orchestration for scheduling & automation.

How to set up EMR clusters, create Glue Data Catalogs, and deploy Airflow DAGs.

Building fault-tolerant and scalable pipelines on AWS.

Performance tuning & cost optimization best practices.

👨‍💻 Who Should Watch
This AWS data engineering tutorial is perfect for:

Data Engineers building ETL pipelines

Data Analysts working with large datasets

Cloud Developers wanting hands-on AWS experience

Anyone learning big data processing on AWS

📂 Prerequisites
AWS Account with permissions for EMR, Glue & IAM

Familiarity with AWS S3, Python & SQL fundamentals

⏳ Chapters
Introduction to AWS Data Pipelines

Overview: EMR, Glue, Airflow

Configure Amazon EMR Cluster

Create & Populate Glue Data Catalog

Build ETL Workflows in Glue

Process Data on EMR (Spark, Hadoop)

Install & Configure Apache Airflow

Create & Schedule Airflow DAGs

Best Practices & Optimization

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