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In our previous Spark Project-Real-Time Log Processing using Spark Streaming Architecture, we built on a previous topic of log processing by using the speed layer of the lambda architecture. We performed a real time processing of log entries from application using Spark Streaming, storing the final data in a hbase table.
In this kafka project, we will repeat the same objectives using another set of real time technologies. The idea is to compare both approaches of doing real time data processing which will soon become mainstream in various industries.
We will be using Kafka for the streaming architecture in a microservice sense.
The major highlight of this big data project will be students having to compare the spark streaming approach vs the Kafka-only approach. This is a great session for developers, analyst as much as architects.
Note: It is worthy of note that the Cloudera QuickStart VM does not have Kafka. We intend to work around that. So come prepare to do Kafka Installation in Cloudera quickstart vm.
In this big data project, we will embark on real-time data collection and aggregation from a simulated real-time system using Spark Streaming.
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.