The debate between single vendor solutions and best of breed approaches has been ongoing for decades in the technology industry. Engineers have always sought out options and choice, and this has led to a shift in the dominance of large vendors in each stage of technological development. As soon as IBM sold enterprises the mainframe solution, engineers started to look for other options.

If you’ve been around the observability world for the past few years, you’ve probably heard a few stats around data growth. Worldwide, data is increasing at a 23% compound annual growth rate (CAGR), per IDC. That means in five years, organizations will be dealing with nearly three times the amount of data they have today – generated by diverse and emerging sources, from data centers to cloud sources to edge computing.

Data Privacy Day is an annual event celebrated on January 28th to raise awareness about the importance of protecting personal information and data privacy. As technology continues to advance and more of our personal information is shared online, it’s crucial for businesses to take steps to safeguard their own data, as well as the data of the customers and users they serve.

Moving to the cloud is hard. Moving to the cloud and keeping systems secure, data governed, compliances met, and cyberattacks at bay, makes everyone’s jobs significantly harder. The number one concern we hear from Cribl customers about the cloud is, you guessed it — security. If you’re in this boat — eager to adopt the cloud ASAP but also worried about the risks that come with having sensitive data in the cloud — don’t fret. We’re here to help.

Congratulations, you’ve worked hard to get Cribl Stream into your technology stack. Buying a new tool is a non-trivial task, so be sure to pat yourself on the back. Now the work starts: You have to deploy Stream and get full value to justify the cost. It’s critical to get started with the right plan to accelerate delivery and maximize the value of Stream. I’m going to start by sharing some ideas about how to get started with Cribl Stream in your first hundred days.

Observability, monitoring, and telemetry are crucial for maintaining the performance and reliability of modern systems. Their concepts are often used interchangeably, but they have distinct differences that are important to understand. In this blog, we’ll explore each concept in detail, including key characteristics and examples of tools. We’ll also compare observability vs monitoring vs telemetry and discuss when it’s appropriate to use each.

You’re responsible for administering hundreds to thousands of server endpoints deployed at your company. You receive daily requests from the application teams requiring agents be installed on new servers, from the compliance team tracking agent upgrades and from the operations team concerned logs and metrics are missing from the dashboards they’re monitoring. You review your workload and realize you must log into each individual server for every request you’ve received.

Data routing is a crucial but complex task for companies of all sizes. Ensuring that the right data is sent to the right tools can be a time-consuming and difficult process, and when things go wrong, it can have costly consequences. This is why having a robust data routing strategy is essential for any organization.

As mentioned in our documentation, Cribl Stream is built on a shared-nothing architecture. Each Worker Node and its processes operate separately and independently. This means that the state is not shared across processes or nodes.This means that if we have a large data set we need to access across all worker processes, we have to get creative. There are two main ways of doing this: In this blog, we’ll walk through how to deploy a Stream leader, Stream worker, and Redis containers via Docker.

Are you just getting started with Cribl Stream? Or maybe you’re well on your way to becoming a certified admin through our Cribl Certified Observability Engineer certification offered by Cribl University. Regardless, using Cribl Stream to send data from one source to many destinations is something you’ll want to try. So if you’re ready, read on!