If you're pursuing a role that requires extensive knowledge of OpenSearch, it’s crucial to prepare in advance. In this comprehensive guide, we will provide an extensive list of the most frequently asked interview questions about OpenSearch and OpenSearch Dashboards, complete with detailed answers.

OpenSearch has grown to be one of the most widely used open-source search engine projects. The high flexibility of the solution enables it to be the perfect option for a broad range of use cases, such as log and event data analysis, application monitoring and metrics analysis, and security information and event management (SIEM).

Today we are announcing the availability of Elasticsearch Serverless in technical preview, which features: Early access customers have used this new self-service option for a range of use cases — from internal analytics to building generative AI applications and conducting machine learning tasks.

Constructing a robust search engine functionality for your application or website is crucial to achieving effective monitoring and analysis. When discussing the best and most appropriate open-source search engines, two particularly popular solutions arise, OpenSearch and Solr. These solutions are very similar, offering the majority of the same features, capabilities, and use cases. However, there are differences between the two search engines that make them better tailored for particular scenarios.

Search powered AI and developer tools built for speed and scale Daily breakthroughs in large language models (LLMs) and generative AI have put developers at the forefront of the movement, influencing its direction and possibilities. In this blog, I’ll share how Elastic's search customers are using Elastic's vector database and open platform for search powered AI and developer tools to accelerate and scale generative AI experiences, giving them new avenues for growth.

Elastic Search 8.13 extends the capabilities that enable developers to use artificial intelligence and machine learning models to create fast and elevated search experiences. Integrated with Apache Lucene 9.10, measured vector search performance has exceeded 2x in benchmarks, extending the sophistication of searches that can be performed in near real time.

An analyst’s process often involves searching through a given set of data many times, refining the question and analytics performed each time. Cribl Search was originally designed to be stateless – executing each search from the original dataset provider(s) with every execution. However, a new feature has been introduced to allow searching into previous cached results, accelerating the analyst process for certain types of iterative search development.

What might you accomplish if you could easily search your data lakes without paying to move the data first? The most likely outcome is that you address a critical security incident quicker than ever, save your organization millions of dollars, get a promotion, and then go down in history as the best-looking, most talented analyst to have searched a storage bucket.

On a scale from walk-in-the-park → scaling Mount Everest, how easy is it for you to search your S3 buckets? Retrieving data to respond to security incidents, demonstrate compliance, or extract insights shouldn’t require jumping through hoops or overpaying for access.Cribl Search has native support for platforms like Amazon Security Lake, Amazon S3, Azure Blob, and Google Cloud Storage, enabling seamless data analysis right at its source.