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Still on the series on Data engineering using Yelp dataset, we have established several concepts - from data warehousing to graph analysis. Well done.
But in today's world, not all data are best stored on HDFS. Some special requirements and scenario could require a data storage with a very low latency that could also handle large dataset. Here comes the use of NoSQL databases.
In this NoSQL project, we will use two NoSQL databases(HBase and MongoDB) to store Yelp business attributes and also learn how to retrieve these data for processing or query. We will substantiate the value of these other ways to store data over using HDFS and how to join them with data stored in HDFS in real time.
Seeing that MongoDB is not available in Cloudera Quickstart VM, we are encouraged to install MongoDB on our host machine while setting up a host network interface between the host and the VM for this big data project.
Explore hive usage efficiently in this hadoop hive project using various file formats such as JSON, CSV, ORC, AVRO and compare their relative performances
The goal of this IoT project is to build an argument for generalized streaming architecture for reactive data ingestion based on a microservice architecture.
This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.