LogDNA is releasing Custom Webhooks, enabling customers to easily integrate LogDNA alerts with 3rd-party services. Custom Webhooks allows you to configure a webhook’s header and body and add specific LogDNA tokens to your alerts going through that webhook.

Logs. You have them. You need to keep track of them. The process of log monitoring can be tedious. Typically it’s one of those things we take for granted and only look at when it stops working. So, how do you deal with them? In this post, we’re going to talk about why and how to monitor logs. Toward the end, we’ll discuss a few tools that’ll help, mostly to avoid manual labor.

Snyk is a developer-centric company whose raison d’être is to identify and patch vulnerabilities in open source security software. With about 50 engineers, Snyk VP Engineering Anton Drukh wants to maintain flexibility in how the team operates. The best way to ensure that is to give them as much insight into their own work as possible, and hence options. They also look at the state of open source security across the industry.

Amazon ELB (Elastic Load Balancing) allows you to make your applications highly available by using health checks and intelligently distributing traffic across a number of instances. It distributes incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, IP addresses, and Lambda functions. You might have heard the terms, CLB, ALB, and NLB. All of them are types of load balancers under the ELB umbrella.

When Splunk was first released almost 20 years ago, it helped many organizations realize the power of logs to gain business insights with pricing based on the volume of data ingested per day. Over the last two decades, the volume, variety, and velocity of data generated by systems and users have grown exponentially. The demands of business and operations have quickly moved beyond compliance and basic reporting.

Remember when you wanted great alerts, so you read our past two blogs about cyclical statistical forecasts and anomalies? Hopefully, the techniques in those blogs gave you some great results. Here we’re going to show you another way of finding anomalies in your data using a slightly different technique.

Over the past 24 months or so, I have been studying investing/trading while also working to become more proficient with Splunk. I like to combine activities and gain momentum, so I decided stock market and economic data would be the perfect way to dig deeper into Splunk and hopefully improve my investing/trading. In the beginning, I only looked at it as a way to learn more about Splunk while using data that was interesting to me.