Elastic Announces Enhanced Access to Nearly Unlimited Data to Help Customers Gain Deeper Business Insights

Elastic (NYSE: ESTC) (“Elastic”), the company behind Elasticsearch and the Elastic Stack, recently announced new capabilities and updates across Elasticsearch, Kibana, and Elastic Cloud in the 7.12 release. The new features are designed to enable users to uncover insights and drive action with their data through the power of search.

Key updates include:

Directly search low-cost object stores with the new frozen tier, now in technical preview

The new frozen data tier makes object stores like Amazon S3, Microsoft Azure Storage, and Google Cloud Storage fully searchable.

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 This data is critical not only for day-to-day success but also for historical reference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 is now 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 is now generally available

Autoscaling is now 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.