I keep track of my daily commutes to and from the office. One thing I want to know is how the different days of the week are affecting my travel duration. But when indexing all my commutes into Elasticsearch, I can not (out-of-the-box) create a histogram on the day of the week. My first visualization will look like this:
At home we have a Youless device which can be used to measure energy consumption. You have to mount it to your energy meter so it can monitor energy consumption. The device then provides energy consumption data via a RESTful api. We can use this api to index energy consumption data into Elasticsearch every minute and then gather energy consumption insights by using Kibana and X-Pack Machine Learning.
The goal of this blog is to give a practical guide how to set up and understand X-Pack Machine Learning, so you can use it in your own projects! After completing this guide, you will have the following up and running:
The following diagram gives an architectural overview of how all components are related to each other:
Indexing document into your cluster can be done in a couple of ways:
Before version 5 however there where only two ways to transform your source data to the document you wanted to index. Using Logstash filters, or you had to do it yourself.
In Elasticsearch 5 the concept of the Ingest Node has been introduced. Just a node in your cluster like any other but with the ability to create a pipeline of processors that can modify incoming documents. The most frequently used Logstash filters have been implemented as processors.
For me, the best part of pipelines is that you can simulate them. Especially in Console, simulating your pipelines makes creating them very fast; the feedback loop on testing your pipeline is very short. Making using pipelines a very convenient way to index data.
LoRa is a wireless modulation for long-range, low-power, low-data-rate applications developed by Semtech. The main features of this technology are the big amount of devices that can connect to one network and the relatively big range that can be covered with one LoRa router. One gateway can coordinate around 20’000 nodes in a range of 10–30km. It’s a very flexible protocol and allows the developers build various types of network architectures according to the demand of the client. The general description of the LoRaWAN protocol together with a small tutorial are available in my previous post.
In the figure above, you can see a simplified model of what a typical LoRaWAN network looks like.
As you can see, the data from the LoRa endpoints, has to go through several devices before it reaches the back-end application. Nowadays there are a lot of tools that would allow us to gather and manipulate the data. A very good solution is the ELK stack which consists of Elasticsearch, Logstash and Kibana; these three tools allow to gather, store and analyze big amounts of data. More information and details can be found on the official website: https://www.elastic.co/.
You work with sensitive data in Elasticsearch indices that you do not want everyone to see in their Kibana dashboards. Like a hospital with patient names. You could give each department their own Elasticsearch cluster in order to prevent all departments to see the patient’s names, for example.
But wouldn’t it be great if there was only one Elasticsearch cluster and every departments could manage their own Kibana dashboards? And still have the security in place to prevent leaking of private data?
With Elasticsearch Shield, you can create a configurable layer of security on top of your Elasticsearch cluster. In this article, we will explore a small example setup with Shield and Kibana.
Some time ago, Kibana joined the elasticsearch family. A lot of good things have come out of it. These days Kibana is becoming more advanced. But with more users also come more demands. One of those demands is more advanced dashboards than can be clicked together in the very nice GUI. We want to be able to customize dashboards, prepare dashboards to be used by others.
In this blogpost I am going to show you some of the options you have to create a more advanced dashboard. I use an index I have created based on my iTunes library. We are going to create a dashboard showing information about artists, albums and we show how to use parameters through the url.
A few weeks ago my colleague Jettro wrote a blog post about an interesting real-life use case for Kibana: using it to graph meta-data of the photos you took. Given that photography is not a hobby of mine I decided to find a use-case for Kibana using something closer to my heart: gaming.
This Christmas I treated myself to a new computer. The toughest decision I had to make was regarding the video card. In the end I went with a reference AMD R9 290, notoriously known for its noisiness. Because I’m really interested in seeing how the card performs while gaming, I decided to spent some time on my other hobby, programming, in order to come up with a video card monitoring solution based on logstash, elasticsearch & Kibana. Overkill? Probably. Fun? Definitely.
I believe it’s also a very nice introduction on how to set up a fully working setup of logstash – elasticsearch – Kibana. Because of the “Windowsy” nature of gaming, some of the commands listed are the Windows version. The Unix folk should have no problems translating these as everything is kept very simple.
If you are reading some technical blogs, maybe about search or data analysis, chances are big you have read about Kibana. You have seen stories about how easy it is to use. Most of the blogging effort deals with getting data into kibana using logstash for instance. Maybe some of you have installed Kibana and are using it in combination with logstash. But what if you want to analyze other data. With the most recent release M4, Kibana is better than ever in analyzing other sort of data. In this blog I am going to show you how to create your own dashboard in Kibana. In order to do something useful with Kibana we have to have data. Peter Meijer had a very nice idea to index metadata from all of your images to learn about the type of photo’s that you take. I decided to put this in practice. I used Node.js and the exiftool to obtain metadata from images and store it in elasticsearch.
