In this article, I’m going to discuss custom logs in Log Analytics. Along with table joins and the let statement that I discuss in another blog, custom logs is a concept that I struggled to wrap my head around for a long time, as there don’t seem to be very many comprehensive guides out there as of yet. Here is a summary of everything I have managed to piece together from documentation and other people’s blog posts.

One of Amazon Web Services’ (AWS) most well-known services is AWS DynamoDB. Some of AWS’s most notable customers use DynamoDB for their database needs – companies such as Netflix, The Pokemon Company, and Snapchat. DynamoDB is relatively simple to set up and configure, and it integrates well with many web-based applications. DynamoDB supports technology solutions in gaming, retail, bank and finance, and the software industry.

Grafana Cloud is the easiest way to get started observing metrics, logs, traces, and dashboards. When we say “easiest,” we mean it: Grafana Cloud is designed so that even novice observability users can use it. As a new user, you are not required to dive into the complexity of setting up Prometheus and figuring out how to create Grafana dashboards from scratch. Integrations are the reason why.

Elastic solutions solve many different business challenges from powering search bars to creating observable systems to detecting and responding to threats. And with the amount of capabilities each offers, learning how to maximize the power of our solutions for enterprise search, observability, and security is critical to realizing Elastic's full value. But finding the time to build new skills can be challenging.

I hope I’m not alone in starting 2021 with some sense of optimism. While several hard months remain ahead of us, I am hopeful and also expecting that some sense of normality will return by the summer months. Either way, this gives us an opportunity to reflect on the challenges we have faced. 2020 was testing. We learnt a lot about ourselves and our businesses in the most challenging of circumstances.

Open source has come a long way. One of my favorite reports on the subject is Red Hat’s State of Enterprise Open Source. For 2020, 95% of respondents said that open source is strategically important to their business needs. Here, I will be recapping my recent Illuminate presentation about embracing open source data collection and I thought it’s important to first talk about how open source has changed.

Syslog is a popular standard for centralizing and formatting log data generated by network devices. It provides a standardized way of generating and collecting log information, such as program errors, notices, warnings, status messages, and so on. Almost all Unix-like operating systems, such as those based on Linux or BSD kernels, use a Syslog daemon that is responsible for collecting log information and storing it.

Oftentimes users of open source are told to go download it and figure it out… or pay for a managed solution in the cloud. So the typical choice is free and do-it-yourself or expensive and easy. With our new changes to Grafana Cloud, we are making it both free and easy to have a real, composable observability solution.

In my ongoing Loki how-to series, I have already shared all the best tips for creating fast filter queries that can filter terabytes of data in seconds and how to escape special characters. In this blog post, we’ll cover how to use metric queries in Loki to aggregate log data over time.

As with many things in 2020, this year’s AWS re:Invent was quite different from any previous iterations. For starters, instead of a week of live talks, face-to-face sessions, and a room full of booths, this year the event was fully online and stretched out for three weeks. As sponsors of this year’s event, we were excited to participate and continue to make an impact on the AWS community.