There’s no question that Elastic has built a truly amazing company, based on the Apache 2.0 open source business model, and on the shoulders of other projects like Lucene. Last week, Elastic announced that, starting with version 7.11, Elasticsearch will now be licensed via SSPL, a license that Mongo released in 2018. So you may be wondering what this all means. Here are what we anticipate will be a few Frequently Asked Questions around this Elasticsearch licensing change.

AWS Lambda is a serverless compute service that runs your code in response to events and automatically manages the underlying compute resources for you. The code that runs on the AWS Lambda service is called Lambda functions, and the events the functions respond to are called triggers. Lambda functions are very useful for log collection (think of log arrival as a trigger), and Coralogix makes extensive use of them in its AWS integrations.

Do you have an application that’s a little… sluggish? Cloud Profiler, Google Cloud’s continuous application profiling tool, can quickly find poor performing code that slows your app performance and drives up your compute bill. In fact, by helping you find the source of memory leaks and other errors, Profiler has helped some of Google Cloud’s largest accounts reduce their CPU consumption by double-digit percentage points.

With Kinesis Firehose being Splunk’s preferred option when collecting logs at scale from AWS Cloudwatch Logs, we’ve seen plenty of posts on setting this up, automation and examples on transforming event content. But what about when things go wrong?

Logz.io has recently launched its Smart Tiering solution, which gives you the flexibility to place data on different tiers to optimize cost, performance and availability. Our mission has been to make Smart Tiering a multi-cloud and multi-region service. As part of this launch, we are glad to announce that the Historical Tier now supports Microsoft Azure Blob Storage, alongside AWS S3.

In this article I’m going to discuss table joins and the let statement in Log Analytics. Along with custom logs, these are concepts that really had me scratching my head for a long time, and it was a little bit tricky to put all the pieces together from documentation and other people’s blog posts. Hopefully this will help anyone else out there that still has unanswered questions on one of these topics.

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.

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.

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.