Operations | Monitoring | ITSM | DevOps | Cloud

Long-term storage, especially for logs, is essential to any modern observability provider. Each vendor has their own method for handling this problem. While there are numerous available solutions, let’s explore just one – DataDog – and see the benefits and limitations.

As an operations engineer (SRE, IT Operations, DevOps), managing technology and data sprawl is an ongoing challenge. Cloud Native Computing Foundation (CNCF) projects are helping minimize sprawl and standardize technology and data, from Kubernetes, OpenTelemetry, Prometheus, Istio, and more. Kubernetes and OpenTelemetry are becoming the de facto standard for deploying and monitoring a cloud native application.

The amount of data organizations handle has created the need for faster data access and processing. Data Denormalization is a widely used technique to improve database query performance. This article discusses data normalization, its importance, how it differs from data normalization and denormalization techniques. Importantly, I’ll also look at the pros and cons of this approach.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.

If the past year has taught us anything, it’s that the more things change, the more things stay the same. The whiplash and pivot from the go-go economy post-pandemic to a belt-tightening macroeconomic environment induced by higher inflation and interest rates has been seen before, but rarely this quickly. Technology leaders have always had to do more with less, but this slowdown may be unpredictable, longer, and more pronounced than expected.