When making investments in our tech stack, we tend to have doubts about companies that don’t use their own products and services. At Elastic, we deploy the full suite of our technology across the enterprise.
https://www.elastic.co/fr/blog/elastic-on-elastic-embracing-our-own-technology?blade=tw&hulk=social
In my role as a Product Lead for Observability at Elastic, I get a few different reactions when I use the term 'observability'. The most common reaction by far today still is: "What is 'observability'?" But I also increasingly hear things like: "We just kicked-off an 'observability initiative', but we're still figuring out exactly how to go about it." And finally, some organizations we have been fortunate to work with already consider 'observability' an integral part of how they design and build products and services.
https://stackshare.io/elastic/observability-with-the-elk-stack
In the previous articles, we learned about setting Fluentd on Kubernetes with the default setup config. Now in this article, we will learn how to create custom indices using Fluentd based on Kubernetes metadata and tweaking an EFK stack on Kubernetes.
The elastic stack has been the go-to solution for log monitoring since quite a while now. Storing your log data inside elasticsearch indices and querying or visualising the data in Kibana is one of the most flexible, secure, high-available and scalable solutions out there. While all of this is still valid when forwarding Kubernetes logs to the elastic stack, there are currently some limitations with the out-of-the-box solution that should be considered.
Elasticsearch query body builder is a query DSL (domain-specific language) or client that provides an API layer over raw Elasticsearch queries. It makes full-text search data querying and complex data aggregation easier, more convenient, and cleaner in terms of syntax.
https://blog.logrocket.com/elasticsearch-query-body-builder-node-js/
Elasticsearch is a highly scalable open-source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time. It is generally used as the underlying engine/technology that powers applications that have complex search features and requirements. Elasticsearch provides a distributed system on top of Lucene StandardAnalyzer for indexing and automatic type guessing and utilizes a JSON based REST API to refer to Lucene features.
https://towardsdatascience.com/an-overview-on-elasticsearch-and-its-usage-e26df1d1d24a
Full-text search can be both scary and exciting. Some popular databases such as MySql and Postgres are an amazing solution for storing data… but when it comes to full-text search performances, there’s no competition with ElasticSearch.
@msvechla @tbragin @TDataScience @michelerivacode