aws-quicksight
There are 47 repositories under aws-quicksight topic.
aws-solutions/clickstream-analytics-on-aws
Clickstream Analytics on AWS source code
aws-samples/bring-your-own-data-labs
Bring your own data Labs: Build a serverless data pipeline based on your own data
nnthanh101/Sentiment-Analysis
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.
DanieleBocchino/AWS-Cloud-Quest
This repository includes some AWS Cloud Quest. it not include the cloud practitioner labs
mincloud1501/DevOps
DevOps에 대한 개념 이해와 AWS 개발자 도구를 활용한 실습 및 연구
AditModi/AWS-Business-Analysis-and-Prediction
Build machine learning-powered business intelligence analyses using Amazon QuickSight
AWS-Big-Data-Projects/front-line-concussion-monitoring-system-using-AWS-IoT-and-serverless-data-lakes
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
AWS-Big-Data-Projects/IoT-Data-with-Amazon-Kinesis
Build a Visualization and Monitoring Dashboard for IoT Data with Amazon Kinesis Analytics and Amazon QuickSight
AWS-Big-Data-Projects/HeartRate-Monitoring-using-AWS-IOT-and-AWS-KINESIS
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 up a table in Athena and use QuickSight to analyze the IoT data.
abduljaleel/workshop-appflow-athena-quicksight
AWS Programming and Tools meetup workshop
Ren294/Covid-Data-Process
This project integrates real-time data processing and analytics using Apache NiFi, Kafka, Spark, Hive, and AWS services for comprehensive COVID-19 data insights.
shogo452/aws-quicksight-tool
aws-quicksight-tool assists in the use of the AWS QuickSight CLI.
JamesJJ/dmarc-report-ses-tsv
Convert DMARC reports to TSV (or CSV) format for easier analysis and visualisation
rochitasundar/TwitterSentimentAnalysis-BigDataProject
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
DivineSamOfficial/SmartCityProject
Smart City Realtime Data Engineering Project
goyal07nidhi/Data-Pipeline
A data pipeline to ingest, process, store storm events datasets so we can access them through different means.
dishadas168/demand-forecasting-ebay
A demand forecasting pipeline deployed on Azure and AWS
shiv-rna/Youtube-Data-Engineering-Pipeline
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.
cevoaustralia/data-lake-demo
Data lake demo using change data capture (CDC) on AWS
evanmathew/Reddit_ETL_DE
This project demonstrates a complete data pipeline for extracting, transforming, and loading (ETL) Reddit data into an Amazon Redshift data warehouse. The pipeline uses various AWS services and tools including Apache Airflow, PostgreSQL, AWS S3, AWS Glue, AWS Athena, and Amazon Redshift. The project is orchestrated using Docker and Apache Airflow
markoshlima/positional-file-process
This project is based for legacy applications that works with positional files to process data. The objetive is read these positional files when they arrives in AWS S3, and then send to a dataware-house like AWS Redshift, and finally read the results with a Business Intelligence tool as AWS QuickSight.
RuFerdZ/Medical-X
US Insurance cost predicting linear regression model. Mainly used to learn about Machine Learning tools in Amazon Web Services (AWS)
SadafAsad/LinkedIn-Jobs-Analysis
Unveiling job market trends with Scrapy and AWS
vbessonov/quicksight-embedding-test
The testbed showing how to embed QuickSight dashboards into a web app
zakkipuar23/Shipper_and_AWS_hackathon
Put-away is one of the most crucial process in supply chain. If we misplace the goods, all of the rest process could be potentially delayed. That's why we choose this process to be improved by multiclassification machine learning model and dashboarding with AWS.
ghfjd/youtube-veri-analizi-sunum
Veri analizi hakkında hazırladığım sunum
lasyakonduru/superstore-sales-data-analysis
Analysis of sales performance and operational efficiency in a superstore using AWS Athena and QuickSight
parth2050/aws-data-pipeline
An End-To-End data pipeline integration from Website Source to analytical dashboard in AWS using Python flask, ML models, DynamoDB and other AWS services.
reyhanhosavci/youtube-veri-analizi-sunum
Veri analizi hakkında hazırladığım sunum
Srinivas39322/Predictive_Analysis_And_Visualization_Pipeline_Using_AWS
AWS Predictive Analytics Pipeline: End-to-end solution for scalable machine learning and visualization in finance using AWS S3, SageMaker, and QuickSight. 🚀📊
Tyriek-cloud/NYC-Mobility-Survey-Analysis
An end-to-end data engineering project in which five NYC DOT datasets were modified in an ETL process and analyzed for insights.
vamsi2792/AWS_Visualize_data
Built a Comprehensive Dashboard using Amazon QuickSight for the Netflix dataset
gachokahassan/Visualize-Data-With-Amazon-QuickSight
Visualize Netflix's TV shows and movie trends using Amazon QuickSight. Includes dataset preparation, S3 integration, and an interactive dashboard for data insights.
Luunynliny/RTE-Consumption-Insights
Daily power consumption and production data retrieving and insights from France.
mihirkudale/youtube-analysis-data-engineering-project
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.