Welcome to another blog post where we explore another exciting dataset with Elastic Stack. Today, you will learn how to ingest and analyze Historical Tornadoes tracks covering 69 years’ worth of data. We will explore the dataset, create the ingest pipeline, consume the CSV file with Filebeat, store tornadoes in Elasticsearch, and visualize them in Kibana.

Covid-19 and its implications are the topic number one for quite some time. The situation naturally provides us a new dataset which any data analyst can process with different tools.

Despite this is not a happy dataset and one I would rather not be part of, it is also an opportunity to use tools, learn new applications, and hope that anything we do will help in some way to other people around us. Whether it is a scientist looking for a different approach to solve the problem or a student learning new tools using interesting data right now, everyone can benefit. Because I believe that we learn by doing ’things’, I am presenting a complete hands-on example based on Slovakia’s data. The same methodology can be applied for similar use cases or just as a proof of concept when needed.

Elastic Map Service nowadays comes with Slovakia regions , which you can happily use with your geospatial data. If you want to have higher precision of Region’s shapes, or you do need to implement the level of ‘districts’ (okresy) or go to the detail of a single city/town, you must use custom map layers. Using different layers is considered a basic task for any GIS application. Possibility to add custom layers in Kibana Maps is available since the introduction and is improving with every release.