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.

In today’s turbulent times, companies big and small are being pushed to do more with less. Budgets are getting tighter and companies are being pressured to serve customers who demand 24/7 availability from their applications and services. To meet these demands and remain competitive, enterprises are adopting cloud-first strategies and developing applications with microservice architectures.

This article was originally published in InfoWorld and is reposted here with permission. The compactor handles critical post-ingestion and pre-query workloads in the background on a separate server, enabling low latency for data ingestion and high performance for queries. The demand for high volumes of data has increased the need for databases that can handle both data ingestion and querying with the lowest possible latency (aka high performance).

In a rapidly changing economic environment, business growth comes with challenges. As organizations grow, some processes become complex and unwieldy, which makes it hard to be agile. Siloed processes create inefficiency and risk. Optimizing processes through purposeful automation can improve employee productivity, boost efficiency, and accelerate time to value—as well as enable a company to change direction rapidly.

DevOps doesn’t necessarily look like it used to. Engineers used to build software designed for on-prem hardware; they had a specific methodology for efficient production and distribution schedules; and they didn’t interface very much with non-engineers, if at all. Today, all that has been flipped upside-down. Cloud-era DevOps engineers now must possess wildly different skill sets, and some previously non-negotiable skills have faded into the past.

CloudCheckr is a SaaS application that helps bring visibility and intelligence to help you lower cloud costs, maintain security and compliance, and optimize resources. The platform supports managing costs between cloud providers like AWS and Azure. This article explores the features and benefits of CloudCheckr in Azure.

Mutual transport layer security (mTLS) is an important subject among security, reliability, and engineering professionals who need to secure API communication as well as communication between machines and the applications and services they run. And for good reason: in 2022, the global average cost of a data breach was US$4.35 million, and almost double that in the United States at US$9.44 million.

Metrics are an important component of monitoring and observability. They provide information about specific durations of measured occurrences. In OpenTelemetry (OTEL), metrics play a huge role in providing visibility into the performance and health of an application. It has become a de facto standard among cloud-native apps for monitoring and observability solutions. Hence understanding OpenTelemetry metrics and when to use them will help you optimize your observability efforts.

Last week, over five hundred SREs gathered in Santa Clara to share the latest research, tips, tricks, best practices, and more for site reliability engineering. They were joined by some of the biggest names in the reliability space. And, yes, Gremlin was there to answer any and all questions about chaos engineering and proactive reliability. After three days of great conversations and insightful talk, let’s take a look at some of the themes we heard weaving through SRECon.

In this post, we will show you how to set up Redis monitoring with SigNoz - an open-source full-stack APM. SigNoz captures data using OpenTelemetry, which is becoming the world standard for instrumenting cloud-native applications. Apart from capturing metrics from your Redis server, you can also capture logs and traces with OpenTelemetry.