This post is a brief summary of a presentation I gave recently where I deploy Elastic App Search, show off the ease of setup, data indexing, and relevance tuning, and take look at a few of the many refined APIs. It’s also written up in a codelab with step-by-step instructions for building a movies search engine app using Python Flask. The app will work on desktop or mobile and is a fast, simple, and reliable way to query the information.

This is part of our series on cost management and optimization in Elasticsearch Service. If you’re new to the cloud, be sure to think about these topics as you build out your deployment. If you are yet to start, you can test out the content here by signing up to a 14-day free trial of Elasticsearch Service on Elastic Cloud.

"Upgrade" can be a four-letter word for admins, so at Elastic, we try to make the upgrade process as simple as possible. Why? Because we pack a ton of goodness into each release, but you can only take advantage of that goodness by being on the latest version of the Elastic Stack. This is also why we make the latest version available on Elastic Cloud the same day that we release.

Modern cloud storage tools such as Dropbox give teams the ability to easily share and centralize content, conveniently collaborate on projects, and sync data across devices. They’ve proven to be real productivity enhancers, especially with the expansion of work-from-home workforces. But cloud storage tools often end up being a dumping ground for lots of content and various clutter, making it clumsy at best (and next to impossible at worst) to find anything.

Searches are integral parts of any application. Performing searches on terabytes and petabytes of data can be challenging when speed, performance, and high availability are core requirements. This blog post will pit Solr vs Elasticsearch, two of the most popular open source search engines whose fortunes over the years have gone in different directions. Both of them are built on top of Apache Lucene, so the features they support are very similar.

In our previous post, we introduced the concept of the Elastic Workplace Search Custom Source API as a means of adding data for which a prebuilt content source integration isn’t available. We used a simple example — a CSV file of contact information — to demonstrate the process along with the use of the associated REST API. In this post, we explore ingesting data from a more complex organizational source: Trello.

With the increasing popularity of Python web frameworks supporting asynchronous I/O like FastAPI, Starlette, and soon in Django 3.1, there has been a growing demand for native async I/O support in the Python Elasticsearch client. Async I/O is exciting because your application can use system resources efficiently compared to a traditional multi-threaded application, which leads to better performance on I/O-heavy workloads, like when serving a web application.

We recently announced that Elastic Enterprise Search — our combined solution of search products — is now available to deploy as a single solution on Elastic Cloud. While Elastic App Search has been available on Elastic Cloud since early 2020, this is a new and exciting deployment option for Elastic Workplace Search.