Operations | Monitoring | ITSM | DevOps | Cloud

The modern enterprise has expanded its reach by using the power of cloud computing. However, with that power comes complexity in leveraging the multiple platforms needed to provide rich functionality. To achieve a seamless integration that involves multiple cloud infrastructures you need insightful and actionable data. You also need the right team to bring the clouds together in a seamless, effective, and efficient manner.

Untangling a chaotic mess can be daunting. And that’s exactly the problem Alain Adler, the Head of Engineering at BetterHelp, faced.

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.