Big data, AI, machine learning, and numerous others are all buzzwords we seem to throw around lightly in recent years. Even though they are hugely different from one another, they all have one thing in common. Data! Huge amounts of data that need to be managed. The downside of that is that the more data you have the more of a headache it is to store, query, and make sense of.

This was a particularly interesting and challenging year for security, cloud computing, and DevOps. It has been exciting to watch organizations and individuals transition to the public cloud and adopt DevOps culture and processes. Many are now reaping the benefits of these technological shifts.

If you are reading this, I don’t have to convince you any further of the powerful intelligence we can derive from logs and machine data. If you are anything like the many, many users, customers and prospects we have been talking to over the years, you might, however, have some level of that pesky modern condition commonly known as volume anxiety. The volume here, of course, is the volume of data––there is a lot of it, and it keeps growing.

Cloudtrail logs provide excellent insight into how your AWS account is being used. They record all activity by the web console, SDKs, and APIs. With help from the AWS plugin, getting this information into Graylog is easier than ever. In this blog post you'll set up the required AWS resources, configure the Graylog input, and do some basic searches to explore its capabilities.

Grafana annotations are great! They clearly mark the occurrence of an event to help operators and devs correlate events with metrics. You may not be aware of this, but Grafana can automatically annotate graphs by querying Loki. Here’s a look at how to use this feature. Loki queries can be used to automatically generate annotations on Grafana dashboards since 6.4.0. For every log line that is returned from a query, the text is automatically displayed as an annotation at the appropriate time.

Thousands of customers leverage the official Elasticsearch Service (ESS) on top of Elastic Cloud to get the best experience for running Elasticsearch — including all of Elastic’s exclusive products such as Elastic Logs, Elastic APM, Elastic SIEM, and more.

Hey, there. This is part four of the Elastic SIEM for home and small business blog series. If you haven’t read the first, second, and third blogs, you may want to before going any further. In the Getting started blog, we created our Elasticsearch Service deployment and started collecting data from one of our computers using Winlogbeat. In the Securing cluster access blog, we secured access to our cluster by restricting privileges for users and Beats.

Today's IT and DevOps teams have not one, but two, feature-rich open source Web servers to choose from: NGINX and Apache HTTP Server (which is often called simply "Apache"). At a high level, both platforms do the same core thing: Host and serve Web content. Both also offer comparable levels of performance and security. Yet when you dive into the details, you'll find that there are many differences between NGINX and Apache.

The last fifteen years have seen huge increases in developer productivity for several reasons, including the arrival of open source into the mainstream and the ability to better emulate target environments. In addition, the process of resetting a development environment back to the last known stable version has been vastly improved by Vagrant and then Docker.