Announcing the general availability of schema
on read, a technical preview of the frozen data tier powered by
searchable snapshots, and support for autoscaling
- Providing unmatched flexibility and speed for customers to get
the maximum value out of their data with schema on read
- Unlocking new value by making object stores fully searchable
with the new frozen data tier
- Adding support for autoscaling on Elastic Cloud to help
customers automatically scale deployments as their resource needs
grow
Elastic (NYSE: ESTC) (“Elastic”), the company behind
Elasticsearch and the Elastic Stack, today announced new
capabilities and updates across its Elastic Enterprise Search,
Observability, and Security solutions, which are built into the
Elastic Stack — Elasticsearch and Kibana.
New features empower customers to reduce the time to extract
value from their data with schema on read, unlock new value by
enabling cost-effective, nearly unlimited storage and search on
object stores with a new frozen data tier, and automatically scale
deployments on Elastic Cloud.
Elastic Enterprise Search users benefit from a number of
architectural enhancements that deliver reduced deployment size,
faster indexing, and more relevant results. Elastic Observability
now includes correlations to help users to identify top drivers of
application performance issues and errors, and Elastic Security
introduces analyst-driven correlation to streamline SecOps
workflows.
Key updates across the Elastic Stack, Elastic Cloud, and
solutions include:
Elastic Stack and Elastic Cloud 7.12
New in Elasticsearch, Kibana, and Elastic Cloud
7.12, users can quickly onboard and flexibly explore their data
with the general availability of schema on read. Now, users no
longer have to choose between the speed and scale of schema on
write or the flexibility of schema on read — they can use both at
the same time, on the same Elastic Stack.
Adding to the innovations announced with the general
availability of searchable snapshots in Elastic 7.11, the new
frozen data tier, now in technical preview, offers the best search
experience while unlocking nearly unlimited data lookback with the
lowest total cost of ownership. The frozen data tier enables
customers to decouple compute from storage, adding the capability
to search directly on low-cost object stores such as Amazon S3,
Google Cloud Storage, and Microsoft Azure Storage. Customers can
search large volumes of data stored on low cost storage with a
tradeoff in performance while reducing the ratio of dedicated
resources needed for search. In the near future, Elastic will also
be offering an enhanced user experience for configuring the frozen
data tier in Elastic Cloud.
Additionally, now in 7.12, users can stay in the flow of
analyzing data while long-running queries complete on their own
with a new “save search to background” feature. Searching across
huge amounts of data in pursuit of the proverbial needle in a
haystack is core to what Elastic technology helps people do.
Long-running search sessions in Discover or on a Kibana dashboard
can be set to run in the background and the new search session
management interface allows users to check progress on results on
demand.
Elastic is also adding enhanced support for autoscaling to help
customers monitor storage utilization and machine learning
capacity, adjust resources, and maintain performance automatically.
As one of the most requested features by the Elastic community,
autoscaling provides a safety net for customers to run their
critical business applications while maintaining node performance
and preventing unexpected costs.
Customers can now also take advantage of improved flexibility
and price/performance with support for new instance types on
Elastic Cloud. Elastic has added Ls-Series instances in the
Microsoft Azure UK South (London) and Japan East (Tokyo) regions,
and D3 instances in the AWS EU (Ireland), US East (N. Virginia), US
East (Ohio), and US West (Oregon) regions. These instances offer
performance value at significant cost savings.
Elastic Enterprise Search
New in Elastic Enterprise Search 7.12, customers benefit
from a reimagined underlying data architecture that drives more
value by reducing deployment size, speeding up indexing, and
delivering more relevant results. The new architecture optimizes
the underlying index management to eliminate data duplication and
employs a new mapping configuration that improves search precision
while maintaining the typo-tolerance that modern search experiences
require. Customers may experience up to 70% improvement in storage
efficiency, up to 40% reduction in indexing latency, and
significant improvements to relevance across App Search and
Workplace Search.
Elastic Observability
In Elastic Observability 7.12, users can now uncover
meaningful patterns in slow application transactions and speed up
root cause analysis with a new correlation capability in Elastic
APM. Elastic APM introduces a new capability that analyzes
application transactions with high latencies and errors and
automatically surfaces factors like service version and
infrastructure metadata that are highly correlated with those
underperforming transactions. With this capability, users can
instantly zoom in on the root cause of poor performance during
reactive troubleshooting workflows, reducing their mean time to
resolution. This capability also drives proactive workflow, helping
application owners identify areas of improvement and continually
improve the end-user experience.
Elastic Security
Analyst-driven correlation, new in Elastic Security 7.12,
is a critical tool for practitioners who need to turn data into
information and insight. Security analysts can accelerate threat
hunting and investigation to surface meaningful data at the speed
of Elasticsearch. The result is more targeted threat hunting and
investigation with higher-fidelity detections derived from the
findings that analysts uncover during those investigations.
Analyst-driven correlation is driven by Event Query Language
(EQL), the technology behind advanced correlation in the Elastic
Security detection engine. While slow response times have
traditionally hampered attempts to boost threat hunting and
investigation with correlation, the ability to now apply
correlations across historical data allows analysts to glean key
insights from the most patient and sophisticated of adversaries in
minutes. Security teams benefit from multiple detection and
investigative methods that cover a broad range of security use
cases. Combining EQL-based correlations with machine learning-based
detections, indicator match type detection rules, and third-party
context at cloud scale enables a more comprehensive security
strategy.
Elastic Security has now also added a new layer of ransomware
prevention with behavioral analysis in the Elastic Agent.
Complementing the signatureless anti-malware first introduced in
Elastic Security 7.9, behavioral ransomware prevention on the
Elastic Agent detects and stops ransomware attacks on Windows
systems by analyzing data from low-level system processes, and is
effective across an array of widespread ransomware families —
including those targeting the system’s master boot record.
Supporting Quotes:
- “As the scope of observability use cases continues evolving,
it's challenging to capture exactly how all users intend on
interacting with data upfront,” said Wes Connell, Security
Engineering Lead, Uber. “The flexibility of runtime fields
provides our users with an enabling solution that's as dynamic as
their data-driven questions.”
- "Schema on read allows us to ask bigger questions of large
security datasets asynchronously so we can find more bad guys,"
said Robert Cooper, VP of Security, Anitian. "With schema on
read and runtime fields, we're able to more quickly respond to
changing data from third-party security tools without going through
the hassle of reindexing our existing data," added Ian Godfrey,
Senior DevOps Engineer, Anitian.
- "Elastic is all about search-driven data exploration to allow
you to gain insights from all your data. Be it threat hunting in
Elastic Security, or advanced correlations to diagnose application
performance issues in Elastic Observability, Elastic enables users
to break through data silos and search, observe, and secure all
their data and applications,” said Ash Kulkarni, Chief Product
Officer, Elastic. "With 7.12, we are bringing greater
flexibility and lower total cost of ownership to this
search-powered process."
About Elastic:
Elastic is a search company built on a free and open heritage.
Anyone can use Elastic products and solutions to get started
quickly and frictionlessly. Elastic offers three solutions for
enterprise search, observability, and security, built on one
technology stack that can be deployed anywhere. From finding
documents to monitoring infrastructure to hunting for threats,
Elastic makes data usable in real time and at scale. Thousands of
organizations worldwide, including Cisco, eBay, Goldman Sachs,
Microsoft, The Mayo Clinic, NASA, The New York Times, Wikipedia,
and Verizon, use Elastic to power mission-critical systems. Founded
in 2012, Elastic is a distributed company with Elasticians around
the globe and is publicly traded on the NYSE under the symbol ESTC.
Learn more at elastic.co.
The release and timing of any features or functionality
described in this document remain at Elastic’s sole discretion. Any
features or functionality not currently available may not be
delivered on time or at all.
Elastic and associated marks are trademarks or registered
trademarks of Elastic N.V. and its subsidiaries. All other company
and product names may be trademarks of their respective owners.
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version on businesswire.com: https://www.businesswire.com/news/home/20210323005964/en/
Elastic Public Relations Ariel Roop PR-Team@elastic.co
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