Elastic enables users to uncover insights and drive action with their data through the power of search
The new features are designed to enable users to uncover insights and drive action with their data through the power of search.
Directly search low-cost object stores with the new frozen tier, now in technical preview
Whether it’s for observability, security, or enterprise search, data can keep growing at an exponential rate. This data is critical not only for day-to-day success but also for historical reference
The frozen tier decouples storage from compute, allowing customers to retain and search their data at a fraction of the cost while also reducing the number of dedicated resources needed for search.
By only fetching the data needed to complete a query from the object store and caching recent queries, the frozen tier offers the best search experience while enabling customers to store and search a nearly unlimited amount of data.
Schema on read generally available
Elastic recently announced the arrival of runtime fields, its implementation of schema on read: a new flexible way to onboard, explore, and search data in Elasticsearch.
While indexed fields, or schema on write, remain the default way to store and search data for speed, runtime fields add the flexibility of defining fields on the fly with schema on read.
With the general availability of schema on read, Elastic is putting runtime fields at users’ fingertips in Kibana.
Not only will fields captured at the time of ingest be displayed (schema on write), but those fields created after ingest with the runtime capability (schema on read) will also be available for analysis.
Autoscaling generally available
Autoscaling is generally available on Elastic Cloud and Elastic Cloud Enterprise. Customers can also take advantage of autoscaling, available in technical preview, in Elastic Cloud on Kubernetes.
Autoscaling monitors the storage utilization of Elasticsearch data nodes as well as the memory consumption of machine learning jobs and automatically adjusts resource capacity.
Customers can set thresholds to cost-effectively manage cluster growth and their Elasticsearch data nodes’ capacity will automatically grow with each data tier as more data is ingested.
In addition, nodes’ memory will scale up or down based on the memory requirements of machine learning jobs to help identify anomalies to support threat hunting and analyze performance issues in customers’ applications and infrastructure.
Stay productive in Kibana by saving long-running searches to the background
Users can benefit from increased productivity in Kibana by saving long-running searches to the background. Hunting for an answer across years of data where the underlying index is living on a frozen asset or spread across remote clusters can create long-running searches.
To aid in handling long-running search, Elastic is introducing a frontend experience in Discover and Dashboard that builds on earlier asynchronous search capabilities to allow users to save a long-running search to the background.
Elasticsearch and Kibana support ARM
Elastic also officially supports ARM aarch64 architectures. The demand for using ARM architectures has increased as testing has shown a more than 20 percent improvement in performance while reducing costs relative to x86-64.
Elastic is committed to pushing the pace of innovation and providing customers with a choice of chip architecture to deploy on.
Improved performance and lower costs with new instance types on Elastic Cloud
New infrastructure enhancements are now available for Elastic Cloud on both Amazon Web Services (AWS) and Microsoft Azure.
On AWS, customers can take advantage of D3 instances in the EU (Ireland), US East (N. Virginia), US East (Ohio), and US West (Oregon) regions.
D3 instances provide high-capacity local storage for dense storage workloads and are designed to deliver additional performance at a lower cost compared to D2 instances. Elastic will also support ARM-based instances in Elastic Cloud soon.
On Microsoft Azure, customers can benefit from Ls-Series virtual machine instances in the Microsoft Azure UK South (London) and Japan East (Tokyo) regions.
Ls-Series I/O optimized instances feature high throughput and low latency. These instances also deliver cost savings of more than 55 percent compared to the previous E-Series virtual machine instances.
Replicate and search across different Elastic Cloud Enterprise environments
Cross-cluster replication and cross-cluster search are available on Elastic Cloud Enterprise, following the release of these features on Elastic Cloud. Whether customers have a single cluster or a globally distributed collection of clusters, they can use cross-cluster replication and search across deployments hosted in any installation.
Cross-cluster replication allows for storing a data copy on one or more other clusters. Even if an entire environment goes down, customers can continue handling search requests using a copy of their data residing in a cluster located in another environment.
They can also reduce search latency by storing a copy of data in clusters that are more closely geolocated to search users.
Cross-cluster search gives customers the ability to seek out data across any number of clusters, regardless of their physical location or whether they are hosted in another environment.
This unified search capability helps break down data silos to derive greater insights by visualizing search results from multiple clusters in a single coherent view.
Enhanced support SLA, more features, and more options for AWS Marketplace users
The Elastic Cloud AWS Marketplace subscription is introducing a number of enhancements that make subscription management easier.
Now, when customers purchase a monthly subscription from within the AWS Marketplace, they receive instant access to Platinum features such as Elastic APM, App Search, and Workplace Search.
Additionally, customers receive access to enhanced support service-level agreements and can change subscription levels directly in the console.