Operations | Monitoring | ITSM | DevOps | Cloud

In the past decade, we've seen explosive growth in the adoption of the cloud-based infrastructure model. IT organizations are increasingly choosing to reduce their up-front investments in IT infrastructure by deploying their applications into cloud environments. These environments offer on-demand availability of data storage and computing power that organizations need to handle high volumes of data and growing demand for application access and services.

FluentD is a free and open-source data collector. With its decentralized ecosystem, it’s known for its built-in reliability and cross-platform compatibility. One of the biggest challenges in big data collection is the lack of standardization between collection sources. They just aren’t able to talk to each other. With FluentD, you can address one of the biggest challenges to big data log collection.

In 2020, the Covid-19-induced lockdown forced all companies to rely on the Work from Home (WFH) policy as an important measure for business continuity. It was an easy transition for the software and IT professionals compared to other industries. However, switching to WFH wasn’t as simple as a one-click operation. There were challenges in WFH, spanning from technical issues to infrastructure setup, as well as in managing the physical and mental well-being of the workforce.

Resilience has become the new strategic imperative for manufacturers during these testing times. As the world’s challenges make headlines, so do the innovative responses of manufacturing leaders. Savvy manufacturers automate, overhaul fundamental processes, modernize their security posture and reduce their CO2 footprint. Forward-focused organizations double down on their cloud investment to become more agile and resilient. And none of it is possible without data.

I’m sure for most of you reading this, you are well aware of just how powerful PowerShell can be. Speaking with partners and prospects over the years who were either using or evaluating our software, I’d often get asked: “Can your products do X?”.

In the realm of big data, Hive is a big deal. Well-written and well-designed Hive queries accelerate data retrieval from datasets. Hive is much better than SQL as the former works with complicated data more effectively. In addition, Hive queries help bring down processing costs. This is why it’s critical to write and optimize Hive queries correctly for big data analytics users and developers.