Operations | Monitoring | ITSM | DevOps | Cloud

In a recent user group meeting, guest speaker Marc Luescher from Amazon Web Services (AWS) joined us to give an overview of Amazon Security Lake. We talked about Cribl use cases and how Cribl Stream can bring your non-AWS data into the Security Lake. Enterprises are dealing with some significant challenges with security data in 2023. Inconsistent, incomplete, poorly-formatted log data is simultaneously scattered across companies and locked up in different silos within the organization.

How you choose to store and process your system data can have significant implications on the cost and performance of your system. These implications are magnified when your system has data-intensive operations such as machine learning, AI, or microservices. And that’s why it’s crucial to find the right data format. For example, Parquet file format can help you save storage space and costs, without compromising on performance.

Accuracy in supply chain operations means so many things. There’s the obvious order accuracy in fulfillment, and all that comes with it — KPIs met, customer satisfaction ratings, etc. Every one of these is important. But there are several ways that improving accuracy affects the business, so we’re looking at how operations teams hit the target with help from voice-enabled applications.

AzCopy is a command-line utility designed for copying data to and from Microsoft Azure Blob and File storage. It is a very powerful tool provided by Microsoft that helps users to copy and transfer data efficiently and securely. One of the key features of AzCopy is the ability to schedule transfers. Scheduled transfers can be extremely useful in managing data and ensuring that data is moved or backed up at the most appropriate times.

Elastic Cloud Observability is the premiere tool to provide visibility into your running web apps. Google Cloud Run is the serverless platform of choice to run your web apps that need to scale up massively and scale down to zero. Elastic Observability combined with Google Cloud Run is the perfect solution for developers to deploy web apps that are auto-scaled with fully observable operations, in a way that’s straightforward to implement and manage.

Cloud deployments and containerization let you provision infrastructure as needed, meaning your applications can grow in scope and complexity. The results can be impressive, but the ability to expand quickly and easily makes it harder to keep track of your system as it develops. In this type of Kubernetes deployment, it’s essential to track your containers to understand what they’re doing.

Keeping employees engaged is a struggle all businesses have to deal with. Managed service providers have a large percentage of technical employees with unique motivators. To engage and retain technical employees, you should keep the following considerations in mind.

Kubernetes, the de-facto standard for container orchestration, supports two deployment options: imperative and declarative. Because they are more conducive to automation, declarative deployments are typically considered better than imperative. A declarative paradigm involves: The issue with the declarative approach is that YAML manifest files are static.