What's Coming with Machine Learning, Logging, and More on Hosted Elasticsearch
Editor's Note (August 3, 2021): This post uses deprecated features. Please reference the map custom regions with reverse geocoding documentation for current instructions.
This post is part of the Elastic{ON} 2018 blog series where we recap specific demos and related deep-dive sessions from the conference. From machine learning forecasting to APM to security analytics with Mr. Robot — check out the list at the bottom of this post.
Want more flexibility in your hosted Elasticsearch deployment? In this demo, Andrew Moldovan showcases customizable deployment templates, coming to your Elastic Cloud or Elastic Cloud Enterprise (ECE) deployment.
What does this mean for Elastic Cloud, the official, hosted Elasticsearch service? Traditionally, Elastic Cloud and ECE users were limited to deploying clusters with a predefined RAM-to-disk ratio. While we’ve always used the best instances available on AWS and GCP, many Elastic Cloud users expressed a desire for greater flexibility in their deployment style.
We’ve been listening. The Elastic Cloud team have worked hard behind the scenes to expand the single RAM-to-disk ratio model to provide more management options for the Elastic Stack, giving you more control over the various aspects of a cluster as you deploy in the cloud.
Got a logging use case? Need warm or hot data nodes? Want to run machine learning jobs? You got it. Click, drag, slide, and you're all set. With the configurable deployment feature, you can manage each part of the Elastic Stack individually. Allocate clusters to specific hardware according to your platform (such as R4, I3, and D2, among others), determine what architecture works best for your needs, click and deploy. It’s that simple.
In addition to configurable deployment, you’ll have the ability to manage individual indices of your deployment in the cloud through index curator. For example, you can tell the index curator to schedule index movement from hot to warm hardware weekly, month, etc. These exciting features are on the horizon for both Elastic Cloud and ECE. We’ve revamped the control console to give you better control and to improve usability within the cloud. Take a sneak peek by watching Andrew Moldovan’s keynote demo.
Want to know more about the new configuration features in Elastic Cloud? In Elastic Cloud.(next) Chris Overton and Alex Brasetvik take a deep dive into Elastic Cloud and Elastic Cloud Enterprise to reveal how customizable deployment features can work for your setup (not to mention a few Kubernetes tips, and a sneak peek at what’s coming in ECE 1.3).
See what else we covered during the conference in these recaps:
- Geo Roadmap for Elasticsearch and Kibana: Layers, GeoJSON, Vega
- But First, Coffee - An Elastic{ON} Canvas Story
- How Mr. Robot's Technical Consultant Used Kibana in the Show
- Monitor Kubernetes with Beats Autodiscover Feature
- Here to Help - An Elastic{ON} Canvas Story
- Data Rollups in Elasticsearch
- App Search with Elasticsearch
- A Preview of SQL in Canvas with Rashid, Creator of Kibana
- Using Kibana and Beats for Security Analytics
- Machine Learning Forecasting on Elasticsearch Data
- APM for the Elastic Stack - A Recap