You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Continuing with my case study on reading a big data file, this is the fifth part of my trilogy :-) on how I got on reading a big'ish file with C, Python, spark-python and spark-scala, AWS Elastic Map reduce and AWS Athena.
The project is to simulate Real-time streaming for movie details using Kafka. We used different technologies such as Python, Amazon EC2, Apache Kafka, Glue, Athena, and SQL.
This project aims to securely manage, streamline, and perform analysis on the structured and semi-structured YouTube videos data based on the video categories and the trending metrics.
This project is about building a data lake and creating an ETL pipeline in Spark that loads data from Amazon S3, processes the data into analytics tables, and loads them back into S3
An end-to-end solution for managing and analyzing YouTube video data from Kaggle, leveraging AWS services and visualized through Quicksight and Tableau
Get the dataset intro a S3 bucket, use AWS glue to transform the dataset, write a Lambda script to clean the dataset, query the dataset via AWS Athena then build a dashboard using AWS Quicksight.