For the past several decades, networks have increasingly become a critical part of business operations. The global pandemic only intensified the importance of networking, with more companies and individuals taking their worlds almost entirely online. This shift also put the spotlight on the need for 24/7, always-on network connectivity.

Increasingly, many organizations find that their current legacy monitoring solutions are no longer adequate in today's modern IT world. These enterprises find themselves struggling to manage and understand unprecedented amounts of data. With such large amounts of data needing to be dealt with, it is no wonder why it's a struggle for enterprises to leverage it for business success. Not to mention that optimizing performance and keeping costs in line is a technical challenge they must face at the same time.

The COVID-19 pandemic ushered in a "new normal," and data center professionals must adapt to keep up with the issues of today while maintaining uptime and business continuity. New challenges include an increased demand on infrastructure with a higher potential for outages, more time pressure on projects, and less staff onsite resulting in a more difficult environment for collaboration, planning new infrastructure and services, and performing changes and maintenance. Data center managers must develop a comprehensive remote data center management strategy to find success.

Whether you are downsizing your infrastructure within a single room or eliminating half of your data center sites, a data center consolidation is a complex, risk-prone project. Fortunately, you can mitigate many of the mistakes and unwelcome surprises that even the most experienced data center professionals find derailing their consolidation.

The Handbook for Kubernetes Errors is an essential guide to understanding and resolving all of the most common Kubernetes issues, including: and many more.

Observability allows you to understand the behavior of applications and infrastructure from the data they produce.

Using Splunk for long-term storage can drive up costs and slow down searches, especially as data volumes increase and teams expand their use cases.