Operations | Monitoring | ITSM | DevOps | Cloud

A deep dive into Elasticsearch authentication realms

This is a technical deep dive into the authentication process — a necessary first step before addressing the authorization decisions that are at the core of Elasticsearch security. The following will be a very detailed explanation of the inner workings of a key part of the authentication process: realms. If you'd prefer to start with a broader view of authentication (and authorization) in Elasticsearch, you may want to check out Demystifying authentication and authorization in Elasticsearch.

Ingest geospatial data into Elasticsearch with GDAL

​Have you used Elastic Maps in Kibana yet? I am very excited about multiple layer support. Heat maps, vector layers from the Elastic Maps Service, and even individual documents all in the same interface! What a fantastic way to analyze and visualize your data. But what about geospatial data that’s not in Elasticsearch? Maybe you want to overlay a shapefile of regional sales territories with sales aggregations.

A Breakdown of Language Analyzers for Elasticsearch

Any search engine needs to be be able to parse language. As the field of natural language processing (NLP) has grown, specific text analysis has been applied to stop words and tokenizing (or marking) them by part of speech. In Elasticsearch (and elsewhere), the most attention has been paid to English, although the ELK stack has built-in support for 34 languages as of this writing.

Demystifying Augmented Analytics

Augmented analytics is trending. You’ve read about it, you’ve heard about it, you may even be in the process of acquiring systems running it. But what exactly is it, and how can you recognize it? As the guys building augmented analytics, we’re here to dispel some of the hype. On the highest level, augmented analytics is the machine learning processes geared at making data more accessible and actionable for both data scientists and business users.

Windows Filebeat Configuration and Graylog Sidecar

Have you ever needed to grab a log from a local server that is not part of the Windows Event Channel? Applications like IIS or DNS can write their logs to a local file, and you need to get them into your centralized logging server for correlation and visualization. Graylog sidecar can help by creating and managing a centralized configuration for a filebeat agent, to gather these types of logs across all your infrastructure hosts.

Application Logs: What They Are and How to Use Them

Within software development, application logging plays an important role. As much as we’d like our software to be perfect, issues will always arise within a production environment. When they do, a good logging strategy is crucial. But what is application logging? How should you be using application logs? Where can you find them? And what does all this mean for your own logging strategy? We’ll take a look at each of these questions in this post.

14 Kibana Plugins to Spice Up Your Data Visualizations

Kibana is a powerful visualization platform designed specifically for log management with Elasticsearch. It already provides a lot built-in, but its open-source nature obviously lends it to some pretty cool simple and complicated additions from its community of devs. Some favorites include adding certain kinds of visualizations, API attachments, better integration between Kibana and other platforms, as well simple add-ons for flair in reports.

Internet Leader Natural Intelligence Now Resolving Glitches in Minutes Rather than Days

Natural Intelligence runs comparison websites that generate millions in ad traffic. A glitch could easily cost the company thousands in ad revenue. CTO Lior Schachter and other members of the NI team share the difference Anodot Autonomous Analytics has made across the company.

Building Consistent Revenue Monitoring with AI

The digital era has brought vast cultural transformations – the sharing economy, microtransactions, lightning-fast communication and much more. Much of this has also resulted in considerable innovation in revenue-related areas. Companies from various industries today manage a large number of revenue streams from different revenue models.