Build clickstream analytics on AWS for your mobile and web applications

Bring your own data Labs: Build a serverless data pipeline based on your own data

Voice of the Customer (VoC) to enhance customer experience with serverless architecture and sentiment analysis, using Amazon Kinesis, Amazon Athena, Amazon QuickSight, Amazon Comprehend, and ChatGPT-LLMs for sentiment analysis.

This repository includes some AWS Cloud Quest. it not include the cloud practitioner labs

DevOps에 대한 개념 이해와 AWS 개발자 도구를 활용한 실습 및 연구

Build a Visualization and Monitoring Dashboard for IoT Data with Amazon Kinesis Analytics and Amazon QuickSight

A simple, practical, and affordable system for measuring head trauma within the sports environment, subject to the absence of trained medical personnel made using Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda

Build machine learning-powered business intelligence analyses using Amazon QuickSight

you run a script to mimic multiple sensors publishing messages on an IoT MQTT topic, with one message published every second. The events get sent to AWS IoT, where an IoT rule is configured. The IoT rule captures all messages and sends them to Firehose. From there, Firehose writes the messages in batches to objects stored in S3. In S3, you set u…

AWS Programming and Tools meetup workshop

aws-quicksight-tool assists in the use of the AWS QuickSight CLI.

This project integrates real-time data processing and analytics using Apache NiFi, Kafka, Spark, Hive, and AWS services for comprehensive COVID-19 data insights.

Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

Convert DMARC reports to TSV (or CSV) format for easier analysis and visualisation

A data pipeline to ingest, process, store storm events datasets so we can access them through different means.

A demand forecasting pipeline deployed on Azure and AWS

This project repo 📺 offers a robust solution meticulously crafted to efficiently manage, process, and analyze YouTube video data leveraging the power of AWS services. Whether you're diving into structured statistics or exploring the nuances of trending key metrics, this pipeline is engineered to handle it all with finesse.

US Insurance cost predicting linear regression model. Mainly used to learn about Machine Learning tools in Amazon Web Services (AWS)

Unveiling job market trends with Scrapy and AWS