Operations | Monitoring | ITSM | DevOps | Cloud

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.

We're excited to announce AppSignal Log Management, a straightforward solution for ingesting and analyzing your logs. AppSignal is designed to be intuitive and help you get the most out of your application's monitoring data. Here's what you'll get with our logging solution.

Has the public sector ever been under so much pressure? Universal across all government departments, essential public services are under significant strain. However, COVID-19 and the subsequent knock-on impacts (economic, social and healthcare challenges) have buckled the resilience and kept many front-line concerned with delivering the scale of service required by the public.