Elastic Announces Optimized Data Architecture, Enhanced Web Crawler, And Autoscaling In Elastic Enterprise Search
Driving Value for Users by Reducing Deployment Size, Increasing Rate of Indexing, and Delivering More Relevant Search Results
- Building on an updated data architecture to deliver greater storage efficiency, search performance, and more relevant results.
- Adding performance and stability improvements to the Elastic App Search web crawler and support for web crawling standards.
- Extending autoscaling to Elastic Enterprise Search to allow users to proactively set rules that monitor storage usage and gain peace of mind.
Elastic , the company behind Elasticsearch and the Elastic Stack, recently announced an updated data architecture, enhanced web crawler, and autoscaling to support consistent search performance, scalability, and relevancy across the Elastic Enterprise Search solution in the 7.12 release.
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Elastic Enterprise Search introduces a reimagined underlying data architecture optimized for performance, relevance, and capacity management. With this new data structure, deployments may benefit from up to 70 percent improvement in storage efficiency, up to 40 percent reduction in indexing latency, and significant improvements to results relevance across App Search and Workplace Search.
The Elastic App Search web crawler, recently introduced in beta, adds several performance and stability improvements, along with better support for web crawling standards such as robot.txt. With the App Search web crawler, users can leverage simple point-and-click tools to extract publicly accessible web content into their App Search engine without any coding required.
Elastic Enterprise Search also inherits index lifecycle management (ILM) policies from the Elastic Stack to automatically manage logs and analytics, and can easily roll additional ILM features into App Search and Workplace Search.
Autoscaling on Elastic Cloud allows Enterprise Search users to proactively set predefined rules that monitor storage usage, whether that storage comes from content, logs, or analytics. When a threshold is met, autoscaling automatically increases customers’ storage capacity based on predefined rules. With autoscaling, users can drive greater insights into their search platform with less overhead.
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