As well as being a search engine, Elasticsearch is also a powerful analytics engine. However, in order to take full advantage of the near real-time analytics capabilities of Elasticsearch, it is often useful to add structure to your data as it is ingested into Elasticsearch. The reasons for this are explained very well in our schema on write vs. schema on read blog post, and for the remainder of this blog series, when I talk about structuring data, I am referring to schema on write.
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.
The issue of unsecured databases is growing. In 2019, 17 percent of all data breaches were caused by human error — twice as many as just a year before. And the IBM/Ponemon 2019 report found that the estimated probability of a company having repeated data breaches within two years grew by 31 percent between 2014 and 2019. Why is this happening?
"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.
The 7.6 release of the Elastic Stack delivered the last piece required for an end-to-end machine learning pipeline. Previously, machine learning focused on unsupervised techniques with anomaly detection. However, several features have been released over the 7.x releases. In 7.2 Elasticsearch released transforms for turning raw indices into a feature index. Then 7.3, 7.4, and 7.5 released outlier detection, regression, and classification, respectively.
Welcome to our series on cost management and optimization in Elasticsearch Service. With the increased functionality in Elastic Cloud, it is now easier than ever to utilise many of the free and open features of the Elastic Stack to optimise your cloud deployment. This blog is a great resource for reviewing your existing high availability and data management strategies when it comes to cost management.
