Thomas Stringer has a couple of great blog posts on how to understand your Azure monitoring costs and also on how to reduce your costs, see Azure Monitor Log Analytics too Expensive? Part 2 – Save Some Money | Thomas Stringer (trstringer.com). In the past I’ve blogged on How to calculate the Azure Monitor and Log Analytics costs associated with AVD (not an easy task!).

In the last few years, many organizations I worked with have significantly increased their cloud footprint. I’ve also seen a large percentage of newly launched companies go with cloud services almost exclusively, limiting their on-premises infrastructure to what cannot be done in the cloud — things like WiFi access points in offices or point of sale (POS) hardware for physical stores.

As companies move more applications into the cloud, and package them into containers, environments become more complex with limited visibility. While infrastructure is abstracted away as much of it is delivered by the hyperscalers, this creates an opaqueness that makes it hard to control costs and understand resource utilization. As a result, many companies are experiencing high cloud bills and lots of cloud waste.

Most Apache Spark users overlook the choice of an S3 committer (a protocol used by Spark when writing output results to S3), because it is quite complex and documentation about it is scarce. This choice has a major impact on performance whenever you write data to S3. On average, a large portion of Spark jobs are spent writing to S3, so choosing the right S3 committer is important for AWS Spark users.

Toward the end of each year, tens of thousands make the journey to Las Vegas to participate in AWS re:Invent. AWS re:Invent has been the seminal conference for cloud-focused engineers since 2012, offering a space where the global cloud computing community can share and learn the latest insights and solutions.

Our goal has always been to automate and standardize the management of infrastructure and deployment of applications across user infrastructure. Today, we are excited to announce that we've launched support for Google Cloud (GCP), expanding our natively supported providers list and further expanding the choice, flexibility, and reach the platform offers to current and new users. With this new integration, users can deploy, manage, and scale their containerized workloads to GCP compute infrastructure.

Heroku Vs. AWS remains a long and persistent debate among developers. Both platforms have strengths and weaknesses. Over the last 10 years, Heroku and AWS played a huge role in the cloud hosting and software development industry, by significantly unlocking productivity in a way that it has never been reached before. They are the platforms behind most of the successes from the last decade. However, we are in 2022 and it still looks like if we (developers) were in 2010 to deploy our apps in the cloud.

The uniqueness of each organization in the enterprise IT space creates interesting challenges in how they need to handle alerts. With many commercial tools in the IT Service Management (ITSM) market, and lots of custom internal tools, we equip teams with tools that are both flexible and powerful. This post is for Google Cloud customers who want to deliver Cloud Monitoring alert notifications to third-party services that don’t have supported notification channels.

When an alert fires from your applications, your team needs to know as soon as possible to mitigate any user-facing issues. Customers with complex operating environments rely on incident management or related services to organize and coordinate their responses to issues. They need the flexibility to route alert notifications to platforms or services in the formats that they can accept.