If you’ve ever found yourself pondering the hidden treasures tucked away within thousands of files in Amazon S3, this is the perfect guide for you. In this blog post, we’re going to look at how you can use the Cribl Search fields feature to catalog and explore the fields in petabytes of data stored in Object Stores. In the Fields Tab within Cribl Search, all returned fields are categorized according to five different dimensions.

The newly released Cribl Search 4.2 brings enhancements that ease data management in today’s complex, cloud-centric environments. This update provides comprehensive compatibility with all major cloud providers – Amazon S3, Google Cloud Storage, and Azure Blob Storage. It also ushers in native support for Amazon Security Lake. In this blog post, we’ll examine how new dataset providers enhance the value that Cribl Search delivers, out of the box.

One of the most exciting things about bringing products to market at Cribl is seeing customers continually find new ways to leverage them to help solve their data challenges. I recently spoke to a customer who described Cribl as the foundation of their data management strategy and a key part of their post-acquisition data engineering process. Let’s take a deeper look into how Cribl can help.

One of our core values at Cribl is Customers First, Always. These aren’t just buzzwords we use to sound customer friendly; it’s ingrained in our daily communication and workload. Without our customers, we wouldn’t exist. One of the ways we’ve upheld this value is to seek out strategic partnerships with other companies aligned with our customers’ needs – both present and future.

A robust observability strategy forms the backbone of a successful cloud environment. By understanding cloud observability and its benefits, businesses gain the ability to closely monitor and comprehend the health and performance of various systems, applications, and services in use. This becomes particularly critical in the context of cloud computing. The resources and services are hosted in the cloud and accessed through different tools and interfaces.

For today’s IT and security professionals, threats come in many forms – from external actors attempting to breach your network defenses, to internal threats like rogue employees or insecure configurations. These threats, if left undetected, can lead to serious consequences such as data loss, system downtime, and reputational damage. However, detecting these threats can be challenging, due to the sheer volume and complexity of data generated by today’s IT systems.

Having your Cribl Stream instance connected to a remote git repo is a great way to have a backup of the cribl config. It also allows for easy tracking and viewing of all Cribl Stream config changes for improved accountability and auditing. Our Goal: Get Cribl configured with a remote Git repo and also configured with git signed commits. Git signed commits are a way of using cryptography to digitally add a signature to git commits.

A recently conducted survey of 51 CISOs and other security leaders a series of questions about the current demand for cybersecurity solutions, spending intentions, security posture strategies, tool preferences, and vendor consolidation expectations. While the report highlights the trends around platform consolidation over the short run, 82% of respondents stated they expect to increase the number of vendors in the next 2-3 years.

Preventing data loss for data in motion is a challenge that Cribl Stream Persistent Queues (PQ) can help prevent when the downstream Destination is unreachable. In this blog post, we’ll talk about how to configure and calculate PQ sizing to avoid disruption while the Destination is unreachable for a few minutes or a few hours. The example follows a real-world architecture, in which we have.

The process of adding new data to operations and security analytics tools is familiar to admins. New data onboarding can be a tiresome process that takes up too much time and delays getting value from the new data. The process typically begins with the admin engaging the data source owner, getting the wrong data sample, and then having to try again.