Welcome to the Elastic Advent Calendar, 2019! A look at Week Two
What a great week it's been! Just under halfway, with 14 posts down and 11 more to go in our 2019 Advent series, you can follow along with the rest of the series by subscribing to our Advent category on Discuss, or watching our @elastic account on Twitter, and of course these recap blog posts (in case you missed it, check out our the Elastic Advent 2019 Week One recap).
From Maps, to SSL configuration, Smart query cancellation, data transforms and Machine Learning, this week is packed full of interesting topics.
Without further delay, here's Week 2 in review
Dec 8 [german]: Loggen in Elasticsearch und Elastic Cloud (logging in elasticsearch and elastic cloud), by Philipp Krenn
Loggen ist eines der Themen, das gerne ignoriert wird, bis man es benötigt. Und dann braucht man meistens eine schnelle Lösung. Glücklicherweise ist das Loggen in Elasticsearch sehr flexibel und auch relativ mächtig. Aber schauen wir uns das gleich konkret an.
Dec 9 [english]: Smart query cancellation in Kibana, by Lukas Olson
Since the beginning, Kibana has always been about taking huge volumes of data in Elasticsearch and making sense of it visually. Over time, Kibana has stretched the limits of Elasticsearch, and we’ve needed to make changes in Elasticsearch in order to more powerfully enable users of Kibana.
Dec 10 [greek]: Κατηγορίες SSL/TLS παραμετροποίησης στο Elasticsearch, by Ioannis Kakavas
Η παραμετροποίηση του SSL/TLS πρωτοκόλλου δεν είναι απαραίτητα το πιο απλό πράγμα που έχει να κάνει ένας διαχειριστής που στήνει για πρώτη φορά ένα elasticsearch cluster. Με την αλλαγή την οποία κάναμε πρόσφατα, όπου η κύρια λειτουργικότητα ασφάλειας (και κατά συνέπεια η υποστήριξη για SSL/TLS ) είναι διαθέσιμη δωρεάν με την βασική άδεια λειτουργίας (basic license), παρατηρούμε ότι όλο και περισσότεροι νέοι χρήστες σπεύδουν να χρησιμοποιήσουν τα νέα αυτά χαρακτηριστικά, θα ήταν χρήσιμο να συζητήσουμε μερικά πράγματα στα πλαίσια του advent calendar.
Dec 11 [english]: Maps getting even easier with 7.5: A practical example, by Radovan Ondas
Kibana Maps were already introduced in version 6.7. Since then each release brought fixes and also many new features and improvements to existing ones.
It was already possible to add color to a location and also custom color map based on chosen document value. This approach is simple and effective for many use cases. Later on we added possibility to use Icons instead of a circle and also coloring based on their value.
With the release of version 7.5 there is a new possibility for how to style your location points you are placing on the layer. As we add many new features to every Kibana release, this little improvement was not even mentioned in the blog post.
Dec 12 [english]: Data Transforms: More than Meets the Eye, by Ken MacInnis
You're running at scale, with petabytes of proxy logs, desktop event streams, and endpoint security alerts at your fingertips. The thing is, human beings don't think about log lines and event fields - we think in terms of users, sessions, and vulnerabilities. But repeatedly querying many indexes across multiple petabytes seems expensive.
There's another option: data transforms, introduced in version 7.3 of the Elastic stack. Data transforms are a way to create summary indexes from existing data, either one time or on an ongoing basis.
Dec 13 [english]: How to diagnose and cure web app flu, by Emanuil Tolev
'Tis the season for festivities, overindulgence and occasional illness. We could suggest season-appropriate decoration 1 (at least if your app is hosted in the Northern hemisphere). However, today we will focus on apps which are feeling unwell and have become sluggish - and how to use APM to diagnose them.
Dec 14 [swedish]: Så här börjar du med Elastic Maskininlärning (Getting started with Elastic Machine Learning), by Camilla Montonen
På Elastic, har vi utvecklat olika maskininlärningsprogram sedan 2016. Då började vi med Elastic Anomaly Detection. Vårt Anomaly Detection program matas med tidsserier och försöker att hitta avvikande datapunkter.
Fördelen med Anomaly Detection är att den inte behöver tränas med märkt data. I stället, lär den sig över tiden vad som är normalt beteende i systemet och övervakar nyankomna data för att hitta avvikelser. Den här är en typ av oövervakad maskininlärning.
Det finns många intressanta problem som inte kan lösas med sådana oövervakade tekniker och i stället behöver övervakade maskininlärningsalgoritmer. Därför har vi i år satsat mycket tid för att utveckla övervakad maskininlärningsfunktioner. I den här korta artikeln bjuder vi på en inledning i övervakad maskininlärning i Elasticsearch.
More to Come
It’s a great collection of content packed into some mighty small space, and we’d love to hear your feedback on the posts. Happy reading!