The News: Open source provider Elastic has launched a number of new features and products in its latest 8.8 release. To read more about Elastic’s announcements, click here.
Elastic Announces New Functionality and Solutions
Analyst Take: As generative AI captures the attention of the masses, both positively and negatively, many vendors are trying to bolt on AI capabilities to gain traction and ride the hype train. However, for other vendors, the recent hype around generative AI is highlighting foundational efforts they have been making in their products to harness the potential of AI. One vendor that falls firmly into the second category is Elastic, the eponymously named company that is built on the foundation of the open-source Elastic code base.
Elastic is taking the same approach to open source as the likes of MongoDB, Red Hat, and SUSE whereby the company looks to provide packaged, tested, and a premium support package for the Elastic code base. This approach is well-proven and has served the company well since the company IPO’d in October 2018.
I have recently had the chance to chat with CEO Ash Kulkarni and Ken Exner, the Chief Product Officer (these interviews will be available soon here) to find out more about the plans the company has to build on its foundational capabilities and harness the opportunities that AI represents for the company.
Elastic 8.8
The company’s core Elastic Enterprise Search was recently refreshed and updated, with the most recent release being version 8.8. This new release offers new semantic search capabilities, along with an expanding catalog of open code database and storage connectors. Put simply, these new capabilities allow customers to uplevel their search experiences with AI/ML-powered relevance, utilize new production-ready, scalable, and open code connectors to unify search across many sources of data, and visualize web and search analytics from the perspective of their web clients.
The most crucial part of the launch for me was the focus on AI/ML-powered relevance, allowing enterprises to enhance search experiences quickly. While the attention in the market is on large language models (LLMs), enterprises will need a search staging point in their stack rather than just adding an LLM to data. With this in mind, the Elastic 8.8 approach can unify search across various data sources using scalable and production-ready connectors.
To simplify and streamline deployment, Elastic Enterprise Search 8.8 is available on Elastic Cloud, which includes all the new features. Customers can also opt for a self-managed experience by downloading the Elastic Stack and other cloud orchestration products.
Another major highlight of Elastic Enterprise Search 8.8 is the introduction of the Elastic Learned Sparse Encoder, an AI model for semantic search. This new model seamlessly integrates with the expanding connector catalog, enabling enterprise search experiences to be powered by the model with a single click.
Previously, achieving AI-powered search required domain expertise and fine-tuning ML models. With Elastic Learned Sparse Encoder, search architecture design can greatly improve relevance and achieve state-of-the-art semantic search without extensive knowledge of ML techniques. Customers can download the model directly or configure and test their pipeline against searchable information.
The 8.8 release focuses on enhancing Elastic Integrations by developing connectors for popular databases, storage, and workplace content platforms in order to ingest various types of data necessary for search applications. Elastic has developed native connectors for the likes of MongoDB, MySQL, PostgreSQL, and Microsoft SQL, which come bundled with a managed ingest pipeline, allowing quick deployment and utilization of ML capabilities. The 8.8 release also offers new connector clients expanded integration options for workplace content platforms like Atlassian Jira, Confluence, and Microsoft SharePoint.
Elastic has also introduced a new feature called Search Analytics, which allows users to measure search success by tracking their query terms, the relevance of their search results, and the popularity of different search applications. Users can customize events and combine data using Kibana dashboards to gain insights from multiple dimensions of their search application. The release also introduces Search Applications, a technical preview feature that simplifies versioned search requests against index/search. Developers can utilize default search templates and presets to combine results from different search methods, abstracting away the complexity and reducing constant updates to search logic.
Overall, Elastic Enterprise Search 8.8 offers bundled capabilities that enhance enterprise search applications from day one. With this software, users can build personalized and AI-powered search experiences with the Elastic Stack and take advantage of intuitive capabilities provided by Enterprise Search.
Looking Ahead
As generative AI captures greater public attention, many companies will ride the wave, none more so than we recently observed with NVIDIA’s barnstorming earnings and subsequent stock jump, and rightly so. Others will quietly go about their business and provide the “picks and shovels” for the AI gold rush. Elastic is well positioned to build on its rich heritage in search and expand from this base into being a foundational technology in the still-nascent enterprise AI space.
I expect the company to continue to bring to market functionality and solutions that provide the building blocks for enterprise adoption of AI against in-house datasets. While this surge in adoption may take a few months to come to fruition, we are seeing the pace of AI adoption accelerate, and this latest 8.8 release will be just a staging point for more widespread adoption.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Other insights from The Futurum Group:
Elastic Reports Fourth Quarter and Fiscal 2023 Financial Results
The Six Five – On The Road with Varun Chhabra at Dell Tech World
The Future of AI is Hybrid: Look No Further than Your Devices to Scale Generative AI
Author Information
Regarded as a luminary at the intersection of technology and business transformation, Steven Dickens is the Vice President and Practice Leader for Hybrid Cloud, Infrastructure, and Operations at The Futurum Group. With a distinguished track record as a Forbes contributor and a ranking among the Top 10 Analysts by ARInsights, Steven's unique vantage point enables him to chart the nexus between emergent technologies and disruptive innovation, offering unparalleled insights for global enterprises.
Steven's expertise spans a broad spectrum of technologies that drive modern enterprises. Notable among these are open source, hybrid cloud, mission-critical infrastructure, cryptocurrencies, blockchain, and FinTech innovation. His work is foundational in aligning the strategic imperatives of C-suite executives with the practical needs of end users and technology practitioners, serving as a catalyst for optimizing the return on technology investments.
Over the years, Steven has been an integral part of industry behemoths including Broadcom, Hewlett Packard Enterprise (HPE), and IBM. His exceptional ability to pioneer multi-hundred-million-dollar products and to lead global sales teams with revenues in the same echelon has consistently demonstrated his capability for high-impact leadership.
Steven serves as a thought leader in various technology consortiums. He was a founding board member and former Chairperson of the Open Mainframe Project, under the aegis of the Linux Foundation. His role as a Board Advisor continues to shape the advocacy for open source implementations of mainframe technologies.
