Operations | Monitoring | ITSM | DevOps | Cloud

Amazon Web Services (AWS) offers the convenience of choosing specific virtual machine combinations to meet your compute needs. Unlike traditional data centers, you can also scale your cloud resources automatically to meet fluctuating workload needs. The best part is that you can rightsize your workload requirements to specific instance types (VM types) offered by AWS. By hand, this takes a lot of time and is prone to errors. This is where AWS Compute Optimizer comes into play.

Cloud migration vs modernization – What are the nuances? Cloud migration involves moving applications and data to the cloud often with minimal changes to their architecture. Cloud migration projects usually aim to leverage cloud infrastructure for benefits such as scalability, flexibility and reduced on-prem maintenance.

The evolution of public cloud over the past few years has been remarkable. Digital transformation, remote work, and AI have created breakneck growth. Back in 2018, before anyone uttered the words COVID or ChatGPT, there were already big drivers for public cloud. The global digital transformation market size was valued at $320 billion, and set for 18% annual growth, to reach a projected $695 billion by 2025.

AIOps is a game changer for MSPs. But how do you implement AIOps to ensure you get those game-changing benefits? Chances are, you’re not interested in spending the resources and time required to build it yourself with all of the development, testing, maintenance, etc. that entails. Instead, AIOps as a service provides you with the capabilities to better manage the IT infrastructure and operations of multiple clients.

In the first article of our series, we explored the importance of trace headers and the complexities involved in their propagation. Now, we shift from theory to practice. This second installment will take you through a hands-on baseline scenario and our initial strategy of propagating the OpenTelemetry trace context in AWS Kinesis by using the PartitionKey parameter.

The emergence of cloud computing. Arguably the biggest change in technology in decades, cloud computing changed how technology would now develop and how businesses and organizations would operate. Indeed, the enormous popularity of cloud services is due precisely to that: you can get different models depending on your operational needs. To properly utilize these cloud service models, you should understand the differences in their functional capabilities and the ideal use cases for each model.