When exploring data, comparing individual data points with overall statistics for a large data set is often useful. For example, you might be interested in understanding when a performance metric rises above the historical average. Or possibly knowing when the variance of that metric increases past a certain threshold. Or maybe noting a change in the distinct number of IP addresses connecting to your public web portal.

Search is a fundamental requirement for anyone working with log files. When you have terabytes and petabytes of data, you need to find answers to questions – fast. The search engine that you choose sits as the cornerstone for any technology that helps you look for the information needed to answer questions. While OpenSearch and Elasticsearch may have similar beginnings, their modern iterations have significant differences.

In this conversation, Cribl’s Carley Rosato talks to Aflac’s Shawn Cannon about his role as a Threat Management Consultant, and how he manages their SIEM environment, brings in new data as needed, and works to improve the ingestion process. Our customers are always coming up with new and exciting ways to implement Cribl tools — importing a 34 million-row CSV file into Redis and enriching events in Splunk might be one of the most impressive we’ve seen so far.

Google Workspace is a robust set of productivity applications with billions of users and millions of paying organizations. These include small mom-and-pop shops and the largest enterprises. Google provides the Google Reports API, “a RESTful API you can use to access information about the Google Workspace activities of your users.” This data is critical for establishing a solid security posture.

Data is growing, and we are being asked to search larger and larger amounts of data. This puts larger and larger demands on Search resources. Reading all the data to find matching events is muscling through the data. Wouldn’t it be more efficient to be able to do filtering before reading the data? Cribl Search does precisely that by leveraging Parquet Pushdowns.

Elastic’s innovative investments to support an open ecosystem and a simpler developer experience In this blog, we want to share the investments that Elastic® is making to simplify your experience as you build AI applications. We know that developers have to stay nimble in today’s fast-evolving AI environment. Yet, common challenges make building generative AI applications needlessly rigid and complicated. To name just a few.

We’re excited to share a recent enhancement made to the Elastic Support Hub: it’s now powered by semantic search! But before we go into more detail on the changes we made to the Elastic® Support Hub and its impact on our customers, it's important that we take a moment to explain the concept of semantic search. At its core, semantic search is a method of search that uses AI to return more relevant search results. Take a look at this quick video explaining the concept.

Workplace Search functionality will merge with Elastic Search in the future. Here’s what you need to know. Recent advancements in generative AI technologies have opened up a wave of possibilities with search. As developers build new experiences, users are adopting new ways of using search — from search queries written in natural language, to searching by uploading images or voice samples.

Wow, can you believe it’s been a year since we announced Cribl Search and redefined the search process for observability and security data? Even though the product is just wrapping up its first year, Cribl Search has achieved unrivaled growth in both user adoption and expansion of product capabilities. Why?