QuickSight artifacts with CDK, CodePipeline, CLI scripts

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

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.

End-to-end simple data analysis in AWS

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

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.

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.

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

QuickSight codes related, specially, with analytics issues

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

AWS QuickSight BI Dashboard

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

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.

A Python script extracts data from Zillow and stores it in an initial S3 bucket. Then, Lambda functions handle the flow: copying the data to a processing bucket and transforming it from JSON to CSV format. The final CSV data resides in another S3 bucket, ready to be loaded into Amazon Redshift for in-depth analysis. QuickSight for visualizations

This AWS-based data pipeline manages data from storage in S3 data lakes, through transformation with AWS Glue and Lambda, to refined storage in separate S3 repositories. Using Athena for SQL querying and QuickSight for interactive dashboards, this solution optimizes data processing and visualization, facilitating informed decision-making and insigh