The last decade has been nothing but a roller coaster ride for the airline industry. The pandemic has transformed it forever and now it needs to reevaluate its digital transformation priorities on how to manage traveler expectations. Taking it a step further, travelers buying behavior is changing farther as now they will want to book tickets while chatting with an AI interface. The transformation was already underway. In 2020, Google Cloud and Sabre announced a partnership to modernize Sabre.

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.

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).