Even before COVID-19 forced nearly everyone to grapple with virtual work, most organizations had mountains of content scattered across numerous teams and dozens, if not hundreds, of apps. But now new research shows the cost of poor employee experiences for organizations: productivity, confidence, and opportunity.
In a previous blog, we covered how you can incorporate synonyms into your Elasticsearch-powered application. Here, I build upon that blog and show how you can combine stemmers and multi-word synonyms to take the quality of your search results to the next level.
TL;DR: We recommend that you use the new point-in-time functionality in Elasticsearch if you can. The scroll API is no longer recommended for deep pagination (even though it still works). Most data is constantly changing. When querying an index in Elasticsearch, you are essentially searching for data at a given point of time.
Honeywell is a Fortune 100 company that produces commercial and consumer products. With roots dating to 1906, the multinational conglomerate offers chemicals, industrial manufacturing, engineering services, aerospace systems, and much more. The United States-based company employs 110,000 workers globally, and posts revenue of nearly $37 billion. Honeywell is a key player in 50 industries. It produces everything from N95 masks to automated warehouse solutions and airport security scanners.
I hate reinventing the wheel once I find a good setup. On top of that, I dislike searching for all the links I used to come up with the “ultimate setup” for different services. So, I decided to outline for myself (and for you of course) my default setup when I deploy on Elastic Cloud to set myself up for success and automate insight for the future. Most of my setup steps make monitoring accessible or automate various warnings to myself.
OpenSearch has been a buzz in DevOps over the first half of 2021. The project is moving forward, but understandably there are a lot of questions. This article will address some of those frequently asked questions, and will be updated to address more over time.
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.
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?
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 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.
