Developing ETL jobs to process YouTube Data using AWS S3, Glue, Lambda, Athena, Redshift and visualizing transformed data with Tableau Desktop
A real-time stock market data analysis project using Apache Kafka⚡, Python, and AWS. It streams live stock data, processes it in real-time, and stores it in AWS S3 for further analysis using AWS Glue and Athena. This project showcases a scalable, cloud-native architecture for handling financial data efficiently
Retrieve data from an api with the data of the frequentation of the cinema in the usa
This project demonstrates the use of Amazon Web Services (AWS) to analyze superstore sales data. The analysis was performed using AWS S3 for data storage, AWS Glue for data cataloging, AWS Athena for SQL-based serverless data querying, and AWS Quick Sight for visualization. The project’s objective was to provide actionable insights into sales trend
"Real-Time Charging Station Utilization and Performance Analytics." Built a serverless ETL pipeline on AWS for batch processing EV charging data Automated data flow using S3, Lambda, Glue, and Athena Enabled real-time analytics with schema discovery and Parquet output Secured and monitored pipeline with IAM and CloudWatch.
Add a description, image, and links to the awsathena topic page so that developers can more easily learn about it.
To associate your repository with the awsathena topic, visit your repo's landing page and select "manage topics."