The Covid-19 pandemic has had an incredible impact on the world as nations work to keep their economies moving, secure the health and welfare of their citizens through social distancing, testing, and an immediate transition to remote work and classrooms. The world is adopting these measures while anxiously waiting for the great scientific institutions and private enterprises to test and deliver a vaccine to eradicate the SARS-CoV-2 coronavirus we know as Covid-19.

Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.

After my last blog around sending Github Data to Splunk via Webhooks, I received a healthy amount of feedback that I want to address here. I learned that (unsurprisingly) a lot of customers are curious about, or dependant on, other cloud platforms out there. In fact, I heard directly from some customers who specifically cannot use any other cloud platforms than one in particular that was not highlighted in my last blog.

As well as being a search engine, Elasticsearch is also a powerful analytics engine. However, in order to take full advantage of the near real-time analytics capabilities of Elasticsearch, it is often useful to add structure to your data as it is ingested into Elasticsearch. The reasons for this are explained very well in our schema on write vs. schema on read blog post, and for the remainder of this blog series, when I talk about structuring data, I am referring to schema on write.

Amazon Kinesis Data Firehose is a service for ingesting, processing, and loading data from large, distributed sources such as clickstreams into multiple consumers for storage and real-time analytics. AWS recently launched a new Kinesis feature that allows users to ingest AWS service logs from CloudWatch and stream them directly to a third-party service for further analysis.

Monitoring your system and infrastructure is critical to ensure the performance of your services. In fact, as software development moves faster and faster, alerting and monitoring becomes an indispensable practice for modern DevOps teams. Why is that exactly? That’s what I’m going to discuss today.

Today’s organizations have the challenge of managing several different applications and software within their technology stack. The more public-facing platforms an organization utilizes, the greater their public attack surface risks. Without proper protection, they and their community can become an easy target for malicious actors.

With the ever-growing volume of application logs and analysis tools available, it can be time-consuming to set up your observability tools to keep up with best practices. Every new piece of infrastructure deployed also causes another piece of dashboard and monitoring that needs to be put in place to ensure stability and reliability.

As you build more complicated Splunk apps, you might wonder, “What is the best way to make the features in my app more usable?” If you’re adding new SPL commands or creating ways to input new data sources, the answer is straightforward. But imagine you’re trying to address one of the following scenarios: For cases like these, consider extending the Splunk REST API with custom endpoints.

With open source in our roots, we’re always excited about integrations with tools like OpenVAS, a popular open source vulnerability scanner that Greenbone Networks has maintained since 2009. If you’re not currently using OpenVAS, you can find the project here. OpenVAS contains more than 50,000 vulnerability tests with a community constantly updating its feed to adapt to the ever-evolving security landscape.