A full data warehouse infrastructure with ETL pipelines running inside docker on Apache Airflow for data orchestration, AWS Redshift for cloud data warehouse and Metabase to serve the needs of data visualizations such as analytical dashboards.

AWS infrastructure visualizer

DAIVI is a reference solution with IAC modules to accelerate development of Data, Analytics, AI and Visualization applications on AWS using the next generation Amazon SageMaker Unified Studio. The goal of the DAIVI solution is to provide engineers with sample infrastructure-as-code modules and application modules to build their data platforms.

Infrastructure metadata scraper with support for K8S, AWS, OpenStack, Terraform and SaltStack for further relational analysis and visualization (which is also included).

A comprehensive AWS multi-account observability platform that aggregates, processes, and visualizes cloud infrastructure data across multiple AWS accounts. Built with serverless architecture using AWS Lambda, Aurora PostgreSQL Serverless v2, and designed for enterprise-scale monitoring with 15+ AWS service integrations, advanced security analytics,

Codebase Analyzer is a Python tool to analyze and visualize codebase structure. It scans directories, detects file relationships, and generates JSON, Markdown reports, and interactive HTML graphs. It supports AWS infrastructure analysis and advanced metrics like dependency cycles and centrality. Licensed under MIT.

Using amplify to visualize AWS current running infrastructure.

AWS Cloud Infrastructure Visualizer - Discovers and visualizes AWS resources across multiple regions with PlantUML diagrams and PNG export.

Cloud Infrastructure Entitlement Management (CIEM) tool for AWS - Discover identities, analyze permissions, assess risks, and visualize access relationships with interactive graph UI

NeuroNews is an advanced ETL pipeline designed to scrape, analyze, and visualize politics and technology news using AI-powered NLP, sentiment analysis, and knowledge graph-based insights. Built on AWS cloud infrastructure, it enables real-time event detection, entity linking, and customizable dashboards for deeper news intelligence.

• Aggregated data from following sources suing the respective application programing interface(API). 1. NY Times 2. Twitter 3. Common Crawl - Applied classical big data analytic method of Map Reduce to the unstructured data collected by the above mentioned sources. - Stored the data collected on WORM infrastructure Amazon Web Services (AWS) S3 AND EC2. - Visualized using Tableau to deduce trends in the processed Data

AWS infrastructure visualization tool

A fun way to visualize your AWS Infrastructure in 3D

Pipeline to loading, transform, store and visualize data in AWS infrastructure

Docker Image with duo-labs/cloudmapper installed to visualize AWS Infrastructure

🌍 Real-time AWS infrastructure visualization dashboard displaying service availability across all global regions with daily updates from AWS Systems Manager Parameter Store

Reverse-engineer AWS infrastructure into production-ready Terraform. Visualize dependencies, detect drift, estimate costs.

Exploratory project using AWS CDK to create and export AWS infrastructure diagrams in draw.io format. A practical example for visualizing cloud architecture.

🔍 Reverse-engineer AWS accounts to visualize dependencies, generate Terraform, and optimize costs with RepliMap's intelligent infrastructure engine.

Automatically generate architecture diagrams from AWS CloudFormation templates. This tool simplifies the visualization of cloud infrastructure by converting YAML-based CloudFormation templates into clear, professional architecture diagrams.

This repository provides a complete solution for extracting, transforming, and visualizing data from the Wallapop API. It utilizes Prefect for ETL orchestration, Streamlit for data visualization, and Terraform for infrastructure management on AWS.

"CloudBank Analytics" is a cloud-based banking data analytics and reporting system that streamlines financial data processing, analysis, and visualization. Leveraging AWS for scalable infrastructure, it uses Amazon RDS for secure data storage, AWS Elastic Beanstalk for web hosting, and AWS Lambda for automated data processing and report generation.

CloudWatchX is a comprehensive AWS infrastructure monitoring solution designed to provide real-time observability, automated alerting, and self-healing capabilities for EC2 instances and related resources. The system integrates AWS-native services like CloudWatch, Lambda, SNS, and CloudFormation with open-source visualization.

The Infrastructure View Generator is a Python-based tool that helps you audit, documentation and visualize your AWS infrastructure. Using Boto3, it retrieves details on various AWS resources such as VPCs, subnets, EC2 instances, security groups, load balancers, and S3 buckets. This is useful for AWS environment documentation and auditing.

: Created Python script for Subreddit API data collection, enhanced and quality-checked using DBT, integrated Amazon Redshift with Power BI for real-time visualization, orchestrated pipeline with Apache Airflow, and ensured scalability via Docker and Terraform for AWS infrastructure.

CloudOps is an infrastructure intelligence platform that scans AWS environments, builds resource dependency graphs, and detects security misconfigurations. It identifies attack paths from internet exposure and visualizes risks through a dashboard. Deployed on AWS using EC2 and S3, it demonstrates cloud monitoring, governance, and automated analysis

A full data warehouse infrastructure with ETL pipelines running inside docker on Apache Airflow for data orchestration, AWS Redshift for cloud data warehouse and Metabase to serve the needs of data visualizations such as analytical dashboards.

This repository provides infrastructure and configuration for building a robust monitoring system in a Private Network and Multi-Account AWS environment. The solution leverages Grafana, Amazon Managed Prometheus (AMP), and CloudWatch with Grafana Agent to collect and visualize metrics for large-scale systems.

This project demonstrates a fully serverless analytics pipeline on AWS where operational data stored in DynamoDB (NoSQL) is analyzed using SQL via Amazon Athena and visualized using Amazon QuickSight. The solution avoids infrastructure management, supports scalability, and enables real-time insights from a NoSQL database using SQL-based analytics.

This study aims to analyze and predict the AWS Honeypot data, which comprises of various country source, host, prototype, and time related data to know the exact attacking patterns of the hackers. Also, the paper draws some regression analysis about further attacks, that could be pre-assumed (known in advance). Several patterns like type of protocol used, locale, peak time of attack and top source address can be addressed specific to a country and target chosen. So, this study helps us to understand the critical information and apply higher protection to those sources that are most targeted. Using concepts of machine learning like regression and analysis techniques we retrieved several out of bound patterns and visualized to produce interesting outcomes from a huge observation. Our contribution can be analyzed about understanding threat intelligence and investigate major attacks in various countries. As a result, the outcomes can be used before preparing a secure infrastructure.