Data is everywhere and there is no doubt that the datasets we now spans gigabytes easily. We live in a data-rich world. Everyone is on the internet and getting meaningful information from queries this data set is critical. Most of the time they get overwhelmed with the data and have trouble finding what they are looking for. Here comes the Search Engines which is designed to search for websites on the internet based on the user's search query.

Personally, I’ve always wanted to contribute to an open-source project, but never found a way to incorporate it with my day-to-day work. Occasionally, I’d muster up the courage to clone a project I liked, seeking a good entry point to add some new feature or handle some issue. I thought that all I needed was to make a small contribution and everything else would just flow into place.

What an exciting episode of OpenObservability Talks it was! On May 27, I hosted Kyle Davis, Senior Developer Advocate for OpenSearch at AWS, for a chat about the OpenSearch project, where it stands and where it’s heading. I wanted to share with you some interesting insights from our chat. You’re more than welcome to check out the full episode.

In January 2021, we announced that starting with version 7.11, we would be changing the Apache 2.0 portions of Elasticsearch and Kibana source code to be dual licensed under Elastic License and SSPL, at the users’ discretion. As part of that change, we created Elastic License 2.0 (ELv2) as a permissive, fair-code license, which allows free use, redistribution, modification, and derivative works, with only three simple limitations, outlined in our original announcement.

Creating an entity-centric index that contains only the latest document for each entity can be useful in a number of situations. For example, maybe you're managing an ecommerce site and you want to track the latest order placed by each of your customers. Or maybe you want to run a campaign targeting customers who haven't been active over a certain period. What's the fastest and most efficient way to compile and organize such data?

In our last episode, I wrote about some speed improvements to date_histogram and I was beside myself with excitement to see if I could apply the same principles to other aggregations. I've spent most of the past few months playing a small part developing runtime fields but eventually I found time to take a look at the terms aggregation.

We are excited to share the latest development in our ongoing partnership with Microsoft. Available in public preview, you can now find, deploy, and manage Elasticsearch from within the Azure portal. Bring powerful enterprise search, observability, and security capabilities to your Azure environment with a user interface and tools that are already familiar to you.

We recently announced that users can find, deploy, and manage Elasticsearch from within the Azure portal. This new integration provides a simplified onboarding experience, all with the Azure portal and tooling you already know, so you can easily deploy Elastic without having to sign up for an external service or configure billing information.

It has been just over two years since we introduced the Elastic Common Schema (ECS), and what a journey it’s been. From categorization fields to request for comments to Threat Intelligence fields, ECS has evolved rapidly over the course of the last two years. In this blog post, I would like to reflect on the ECS journey so far, and look towards the future of ECS.

I’ll come clean and admit it – this part of the series will be a bit interesting given the fact that I know very little about Elasticsearch. So really, this is an honest test of the question – “can I still build something good with Dashboard Server even if I only have nominal knowledge of the tool where the data is sourced from?”