Build a Complete AWS Data Pipeline: Yahoo Finance Stock Data ETL Project

⬇️ Click here to learn how to land a high paying data engineering role NOW ⬇️ https://dataengineerinterviews.com/op... Click here for access to the project documentation: https://drive.google.com/drive/folder... Build a complete AWS data pipeline from scratch in this hands-on data engineering project tutorial. Learn how to extract stock data from Yahoo Finance API, transform it, and load it into AWS using industry-standard ETL practices. Perfect for anyone taking a data engineering course or building a portfolio project to land their first data role. If you're enrolled in a data engineering course, looking to build real-world projects for your resume, or want to understand how professional data pipelines work in production environments, this step-by-step tutorial will teach you the exact skills companies expect from data engineers in 2025. This tutorial is ideal for: Students enrolled in data engineering courses building portfolio projects Career switchers learning AWS data engineering skills Data analysts transitioning to data engineering roles Software engineers adding cloud data pipeline skills Anyone completing a data engineering bootcamp needing real projects Self-taught data engineers building resume-worthy projects Professionals preparing for data engineering interviews Skills you'll practice (commonly required in data engineering courses): AWS Services: S3, Secrets Manager, IAM Python for data engineering ETL pipeline architecture API integration and data extraction Cloud security best practices AWS CLI configuration Docker basics for data engineering Why this project matters for your data engineering course or career.... This is the exact type of project hiring managers look for when reviewing candidates from data engineering courses and bootcamps. You'll learn how to: Extract data from external APIs (Yahoo Finance) Transform and clean data using Python Load data into cloud storage (AWS S3) Secure sensitive credentials properly Build scalable, production-ready pipelines Create documentation-worthy code for your GitHub portfolio Perfect companion project for data engineering courses covering: AWS fundamentals, Python for data engineering, ETL pipelines, cloud data warehousing, data pipeline architecture, and portfolio development. If you’re new to my channel, my name is Christopher Garzon. I run the top Data Engineering Academy in the country, where we help students transition into data engineering from other data professions to increase their compensation. How I got here… At 18 years old, I started at Boston College. At 20, I was sneaking into graduate-level classes to take machine learning and data science courses. At 21, I invested in a data science course from a mentor and wired him $3,000 without ever meeting him. At 22, I landed my first job as a data analyst at Amazon, making $60,000 per year. At 24, I became a data engineer at Amazon, increasing my salary to $100,000 and started angel investing in a couple of data companies. At 25, I moved to a startup as a data engineer and doubled my income to $200,000 per year. At 26, I was making about $350,000 at Lyft. At 27, Lyft stocks went up, and my total compensation reached around $450,000. That same year, I launched the Data Engineering Academy. For the last two and a half years, I’ve been running the Data Engineering Academy full-time, helping thousands of people transition into data engineering and significantly increase their earning potential. To all the data professionals grinding—your journey is still being written. The bigger the obstacles, the greater the story. Remember, don’t settle for your next job. Go for a better one. Chris 00:00:00 - Introduction & Project Overview 00:01:15 - Prerequisites: AWS CLI, Docker, AWS Account Setup 00:02:30 - Project Architecture Explanation 00:04:45 - Setting Up Yahoo Finance API Key 00:07:20 - Storing API Key in AWS Secrets Manager 00:10:30 - Creating S3 Bucket Structure (Raw & Transform Folders) 00:12:45 - Installing & Configuring AWS CLI 00:15:00 - Creating IAM User & Access Keys 00:17:30 - Configuring AWS Credentials in Terminal 00:19:00 - Python Script Setup: Importing Libraries 00:21:15 - Creating Get Credentials Function 00:24:30 - Building Fetch Secret Function (Secrets Manager) 00:27:45 - Writing Fetch Stock Details Function ⬇️ Click here to learn how to land a high paying data engineering role NOW ⬇️ https://dataengineerinterviews.com/op...