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Research Note: AutoOps for Elastic Cloud

IT Ops

Elastic recently unveiled AutoOps for Elastic Cloud, an advanced management tool from Elastic’s Opster Team that simplifies Elasticsearch cluster administration through automation, real-time insights, and optimization.

Based on Opster technology and integrated directly into the Elastic platform, AutoOps empowers users by reducing the time and expertise needed to maintain optimal Elasticsearch performance. This makes it an attractive solution for organizations reliant on search and analytics capabilities.

Research Note: Elastic SIEM Solution

At the 2024 RSA conference, Elastic announced that its AI-driven security analytics solution, part of the Search AI platform, will supplant traditional SIEM systems in modern SOCs. The solution leverages search and retrieval-augmented generation (RAG) to streamline the previously manual configuration, investigation, and response processes, delivering hyper-relevant results swiftly.

Research Note: Elastic’s AI-Focused SIEM Updates

At the 2024 RSA Conference, Elastic introduced significant enhancements to its Security Information and Event Management (SIEM) solution, Elastic Security. The upgrades, revealed at the recent RSA Conference, are a substantial leap in the evolution of security operations centers (SOCs).

Elastic Shows Robust Growth on the Back of GenAI & Cloud Expansion

Elastic demonstrated an impressive performance in its latest earnings release, handily beating consensus estimates for revenue and EPS, and even surprised Wall Street with its forecast. The earnings showed Elastic making significant strides across every segment of its business.

Research Note: Elastic Fiscal Q2 2024 Earnings

Elastic this week released its fiscal Q2 2024 earnings, handily beating consensus estimates for both revenue and EPS. This Research Note explores Elastic’s earnings for the quarter, taking a special look at the impact of generative AI on the company’s business.

Elastic’s Elasticsearch Relevance Engine Enables Generative AI Search

Background The challenge for an enterprise wanting to harness the power of large language models (LLMs) is that a language model is only as capable as the data it’s trained on and understands. This hampers the ability to leverage the technology to solve real-world business problems. LLMs become infinitely more powerful when deeply integrated with […]