Scaling cloud services is a critical task for Site Reliability Engineers, and it’s a challenging one. As organizations grow, the amount of data and the number of users of it grow like crazy, pushing traditional data management methods to their limits. SREs not only have to keep everything running, they’ve got to make sure it runs smoothly, efficiently, and swiftly.

Cribl is an integrations company at heart. We want to help every company develop a data strategy that gives them more control, improves security, and provides flexibility to adapt to their ever-changing data needs. Today, we’re thrilled to announce that we are a Certified Wiz Integration (WIN) Partner to help customers take their cloud security game to the next level.

This is the first in a series of blog posts about the disconnect between modern IT and security teams and the vendors they’re forced to work with. If you’re looking for the second and third posts, you can find them here and here. Imagine this scenario: You’re grappling with the ever-escalating costs of your legacy solutions. What’s the logical next step? For many, it’s exploring the new wave of tools emerging, such as data warehouses.

As the cybersecurity landscape becomes more and more complex. It seems like we hear about a major breach of a different company every day. Enterprises are looking for robust solutions to help them manage the surge in data and security incidents. That’s why our recent collaboration announcement with Microsoft means so much to us. It’s not just a piece of paper; it’s a testament to our dedication to providing customers with the best tools and solutions for the job.

This blog post explores Cribl.Cloud‘s approach to Identity Governance (IG), a crucial strategy for securing access to critical systems and data. Learn how Cribl.Cloud leverages IG to ensure security, compliance, efficiency, and customer trust, while also tackling the challenges of managing custom SaaS APIs within an IG framework.

In this livestream, I talked to security expert Matt Johansen, a computer security veteran who has helped defend everyone from startups to the largest financial companies in the world. We talked about the current state of cybersecurity, why attacks are on the rise, and what can be done to prevent threats in the future. Matt’s blog covers the latest news in cybersecurity and also touches on mental health and personal growth for tech professionals.

Without collaborations between organizations, the tech industry wouldn’t be where it is today. Customer expectations and needs don’t exist in a silo. They need their tools to work together to solve problems and deliver value regardless of the vendor. With data growth at a 28% CAGR and cybersecurity threats on the rise, customers need their entire suite of tools working for them in a cohesive manner.

What do you need to achieve observability? Who you ask and the role they hold will influence the answer, but the answer likely follows this pattern: “You only need is how you define observability.” I cannot disagree with this logic. A specific use case may only need a specific type of telemetry. Experience and expertise allow engineers to quickly answer questions about a system without expanding into adjacent data types.

Mainframes might seem like relics of a bygone era to many of us in 2024, but the truth, however, is far from that. Despite their reputation as ancient behemoths—and frequent targets of jokes—mainframes continue to be vital powerhouses driving the global economy. Their capability to process billions of transactions daily, including the majority of credit card transactions, underscores their enduring significance.

There is an immense amount of IT and security data out there and there’s no sign of slowing down. Our customers have told us they feel like they’re drowning in data. They know some data have value, some don’t. Some might have value in the future. They need some place cost-effective to store it all. Some for just a short while, some for the long haul. But they’re not data engineers. They don’t have the expertise to set up and maintain a traditional data lake.