QuickSight artifacts with CDK, CodePipeline, CLI scripts

This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset.

Copy data from Azure Blob Storage to Amazon S3 using code. View Azure costs using Amazon QuickSight

End-to-end simple data analysis in AWS

Airflow orchestrated ETL (running in docker containers) that pulls batch data from an API to a local Postgres database, loads to AWS S3/Redshift provisioned by Terraform, and visualized in Quicksight.

Conducted real-time Twitter sentiment analysis using Spark, S3, VADER, Athena, Amazon Quicksight processing over 113,000 tweets for insights and create dashboards for visualization.

Display embedded quicksight dashboard using Cognito connected user

This project outlines the final project requirements for DAV6100 - Information Architectures, focusing on group assignments, scoring criteria, topic selection, core requirements, and project components such as design, development, visualization, and executive presentation.

The project aims to develop a real-time analysis system using Apache Kafka and Apache Spark with cloud-based architecture.The system will collect real-time data and stream the data into Kafka. Apache Spark will then be used to process and analyze the data in real-time.The processed data will be visualized using appropriate visualizations and graphs

QuickSight codes related, specially, with analytics issues

Helps copy AWS QuickSight Dataset, themes and Dashboard to different account

AWS QuickSight BI Dashboard

Riptwitter was trending on twitter when Elon Musk took charge. Lets collect tweets under the hashtag using Twitter API and analyze the tweet sentiment

This project builds a pipeline to analyze Superstore sales data using the power of AWS. It transforms the data to make it ready for exploration. Querying the transformed data using SQL queries to uncover trends and patterns. Analyzing results and creates easy-to-understand visualizations, providing clear insights into Superstore sales performance.