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A while back, we did web server access log processing using spark and hive. However, that processing was batch processing and in the lambda architecture, we will only be able to operate in the batch and serving layer.
In this big data project, we are going one step further by bringing processing to the speed layer of the lambda architecture which opens up more capabilities. One of such capability will be ability monitor application real time perform or measure real time comfort with applications or real time alert in case of security breach.
The abilities and functionalities will be explored using Spark Streaming in a streaming architecture.
Note: It is worthy of note that the Cloudera QuickStart VM does not have Kafka. However, like in our objective, we will make the case for using Kafka but our implementation will not be using Kafka. Instead, we will integrate the log agent with Spark streaming in this big data project.
In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.
In this big data project, we will talk about Apache Zeppelin. We will write code, write notes, build charts and share all in one single data analytics environment using Hive, Spark and Pig.
This Elasticsearch example deploys the AWS ELK stack to analyse streaming event data. Tools used include Nifi, PySpark, Elasticsearch, Logstash and Kibana for visualisation.