This project aims to show some cool intresting facts, records, results of IPL
-
Updated
Apr 23, 2020 - Python
This project aims to show some cool intresting facts, records, results of IPL
Live sentiment analysis of tweets using Kafka
Implemented the following framework using Apache Spark Streaming, Kafka, Elastic, and Kibana. The framework performs SENTIMENT analysis of hash tags in twitter data in real-time. For example, we want to do the sentiment analysis for all the tweets for #trump, #coronavirus.
PoC for setting up an ElasticSearch Cluster, to serve APIs built in Flask
Containerized: ElasticSearch + Kibana
ELK "Beat like" monitoring daemon for AIX (Metricbeat and Filebeat / Python3)
This project involves extracting information from the NYC public dataset, loading it onto AWS cloud using Docker, and conducting analysis through Kibana. It focuses on analyzing 20 years (8.6 million records) of NYC Fire Incident Dispatch data. The dataset covers fire incidents across New York City, and the goal is to gain insights from dataset.
Tweets Streaming and visualization
Python client for kibana. Provide ORM & vega rendering of visualizations
Centralized Log Management - Fastapi, Elastic Search, Logstash, Kibana and Docker.
Add a description, image, and links to the kibana-visualization topic page so that developers can more easily learn about it.
To associate your repository with the kibana-visualization topic, visit your repo's landing page and select "manage topics."