Operations | Monitoring | ITSM | DevOps | Cloud

Since 2018, General Data Protection Regulation (GDPR) has been on everyone’s lips – and for good reason. It’s the gold standard for consumer protection. While privacy laws present challenges to enterprises big and small, we can’t escape the reality that consumer data is constantly mined and sold.

Gartner recently released a report highlighting the top trends in cybersecurity for 2022. They discuss how businesses must reframe their security practices, rethink their technology, and adopt new responses to modern threats. This is in line with DevSecOps trends that we see in the market and hear about from our customers. Companies that are working in this way are taking a more holistic approach to cybersecurity by adopting tools that speed up and, when possible, automate security.

It’s impossible to ignore AWS as a major player in the public cloud space. With $13.5billion in revenue in the first quarter of 2021 alone, Amazon’s biggest earner is ubiquitous in the technology world. Its success can be attributed to the wide variety of services available, which are rapidly developed to match industry trends and requirements.

It’s common sense. When a logstorm hits, you don’t want to be left scrambling to find the one engineer from each team in your organization that actually understands the logging system – then spending even more time mapping the logging format of each team with the formats of every other team, all before you can begin to respond to the incident at hand. It’s a model that simply won’t scale.

Source-side queueing is a fancy way of saying: You can configure Cribl products to make sure data isn’t lost in the event of downstream backpressure, again. Those familiar with Cribl Stream might be aware of destination queuing or persistent queuing, wherein Stream can write data to the local disk in the event of an issue reaching the destination. Maybe your SIEM is suffering from disk I/O latency. Maybe there is a DNS problem with your load balancer (Hint: It’s always DNS).

I don’t care about religious wars over “which logger is the best”. They all have their issues. Having said that, the worst logger is probably the one built “in-house”… So yes, they suck, but re-inventing the wheel is probably far worse. Let’s discuss making these loggers suck less with proper usage guidelines that range from the obvious to subtle. Hopefully, you can use this post as the basis of your company’s standard for logging best practices.

When IT operators and architects begin their journey with Google Cloud, Day 0 observability needs tend to focus on infrastructure and aim to address questions about resource needs, a plan for scaling, and similar considerations. During this phase, developers and DevOps engineers also make a plan for how to get deep observability into the performance of third-party and open-source applications running on their Compute Engine VMs.

It’s been a week. A long week. After the most recent Board of Directors meeting, your senior leadership tasked you with finding a security analytics solution. Over the last month, you’ve worked with leadership to develop some basic use cases to determine which solution meets your security and budget needs. You started your research, but everything on the market seems really overwhelming.

In our ongoing efforts to provide our customers with a best-in-class log management solution, we’re excited to launch improvements to our Agent. This new, robust version (3.4) provides significant performance improvements for our customers. Specifically, we focused on two main areas of the Agent: error handling and endurance.