Analytics

In the previous article (Part1), we installed the ELK stack along with the ES-Hadoop connector and spark, then we did some visualizations in Kibana with the houses price prediction data set from kaggle. In this part we will start with adding Search Guard to the stack in order to define permissions and access to our data and configurations, then we will implement our models with the help of Spark Ml lib, and we will finish with deploying our models in a pipeline in order to predict the prices for new entries to our Elasticsearch. ...

Before digging into any technical details, I will start with brief descriptions of the tools that I will be using for the tutorials (this part and the coming ones). Cover Photo by Marius Masalar on Unsplash 1 — E(Elasticsearch).L(Logstash).K(Kibana) Stack ! The ELK Suite is an acronym for a combination of three widely used open source projects. E = Elasticsearch (inspired by Lucene), L = Logstash and K = Kibana. All developed in Java and published as Open Source under the Apache license. The addition of Beats turned the stack into a four-legged project and led to its renaming as “Elastic Stack”, but for us in this article we will at least use the official name of ELK. ...