This is in continuation of the previous Hive project "Tough engineering choices with large datasets in Hive Part - 1", where we will work on processing big data sets using Hive.
Use the dataset on aviation for analytics to simulate a complex real-world big data pipeline based on messaging with AWS Quicksight, Druid, NiFi, Kafka, and Hive.
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.
This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.
