Your first reaction might be “why not use the Timelion plugin or more recently Visual Builder with Kibana instead?” We understand that Timelion is a good step toward turning Kibana into a legitimate Time Series Database (TSDB), but it still needs work. It will be interesting to see how Timelion closes the gap in this department.

The sheer options and flexibility to manipulate the data into gorgeous visualizations coupled with the open source community’s pre-made dashboard make Grafana an excellent choice or alternative to Kibana’s offerings.

In the previous tutorial we covered how to successfully integrate one of the most popular, open source, TSDB visualization tools, Grafana with Elasticsearch. In this tutorial we will use Grafana to monitor an Elasticsearch cluster by importing an existing community dashboard along with our additional customizations.

It’s finally here. A legitimate contender to Grafana in the time series DB visualization space. Time series analysis with Kibana has been at least a few steps behind Grafana, even with Timelion. Time Series Visual Builder, however, levels the playing field with its own set of visualization customizations not seen in Timelion. In this tutorial, we will install and deploy Kibana and Metricbeat on a QBox cluster and play with the latest Visual Builder feature!

This tutorial assumes you have already spun up a QBox cluster. If not, sign up, and spin one up!

In the previous tutorial, we learned how to set up a Qbox Cluster with the ES-Hadoop connector to interface with Hadoop’s data warehouse component, Hive, to perform SQL queries on top of Elasticsearch. The benefits of offloading and manipulating ES indices with Hive enable a multitude of possibilities for high-performing, deeper analysis across large data sets.

In this tutorial we will take it a step further, by using Logstash to import an existing data set in the form of a CSV file into Elasticsearch in order to perform later batch-analytics in Hadoop’s powerful ecosystem.

For the past four years or so, the term “Big Data” has been loosely thrown around marketing and tech conferences, publications, blog articles, and everywhere in between. The buzzword has since been defined and classified, but one particular distributed storage and processing ecosystem might as well be synonymous with it as well: Apache Hadoop.

Hadoop is composed of a very wide array of packages and tools that can bulk ingest and process data with the power of distributed clusters of commodity hardware and/or container technologies. So it comes as no surprise that organizations have been combining the power of Hadoop to perform deeper analytics and produce “actionable insights” with Elasticsearch for robust log and performance metric analysis.

In this tutorial, we shall utilize the Elastic Hadoop connector to integrate Elasticsearch with a Hadoop cluster and introduce readers to how external tables in Hive work with Elasticsearch mappings and bulk-loaded docs.