Operations | Monitoring | ITSM | DevOps | Cloud

Almost three years into the hybrid working experiment and for some, the unintended pilot has turned into an adopted model, while for others the IT complexities of dealing with a remote workforce remain a persistent headache. Although hybrid or remote working are not new concepts, there are several reasons it wasn’t a widely adopted model prior to the outbreak of the pandemic in 2020. Many of those reasons are cultural, but some are purely technical.

It is so low-tech that Gen Z’ers and other digital natives may faint (or perhaps the avatar in a VR game that they are playing may faint) to learn that one of the greatest inventions in the history of our species is the humble do-not-disturb sign. Indeed, this magical placard is like having a very own private Gandalf shouting: YOU SHALL NOT PASS! However, the glory of do-not-disturb is not limited to hotels, motels, and teenagers’ bedrooms.

There’s a reason why the network monitoring market reached about $2 billion in 2019 and is expected to reach $5 billion by 2026. In today’s tech-focused world, organizations require network monitoring to secure and manage their IT infrastructures. One of the crucial systems that every network monitoring and management operation needs to succeed is known as network discovery.

When we introduced Grafana Mimir into the open source ecosystem, we weren’t shy about our ambitions. Once we got past answering some of the easier questions (For the record, the name Mimir comes from Norse mythology, and it’s pronounced /mɪ’mir/.), we quickly got to work making good on our promise to deliver the most scalable, most performant open source time series database (TSDB) in the world.

System hardening means locking down a system and reducing its attack surface: removing unnecessary software packages, securing default values to the tightest possible settings and configuring the system to only run what you explicitly require. Let’s take an example from daily life.

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.