A hybrid infrastructure brings business benefits but it also brings new challenges. Migrating workloads to the cloud is a complex operation that generates more data than engineering teams can adequately manage. Traditional monitoring tools are limited in helping teams find and fix problems during and after a cloud migration. This can throw business strategies off course, limit customer value and hurt the bottom line.

In the software industry, monitoring and observability come hand in hand and are referenced simultaneously, but both are not the same thing. Having the right tools for these capabilities is vital to keep your systems, data, and security perimeter safe. With cloud solutions topping up in the integration space, great tools have been available in the market, and choosing the right one would be challenging.

Most customers running Kubernetes clusters Amazon EKS are regularly looking for ways to better understand and control their costs. While EKS simplifies Kubernetes operations tasks, customers also want to understand the cost drivers for containerized applications running on EKS and best practices for controlling costs. Anodot has collaborated with Amazon Web Services (AWS) to address these needs and share best practices on optimizing Amazon EKS costs.

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.

Earlier this year, a new user management system was announced, designed to give Spot’s users more granular control on their Spot resources at both the organization and account levels. As we got very good feedback from customers, we are now happy to announce a new feature in the user management system called Custom Policy Conditions. The condition feature allows users with policy modification access to create conditions within the policy.

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.

MongoDB has evolved into the database solution of choice for developers looking to build efficient, scalable applications in the cloud. But, as when using any cloud-based infrastructure, using MongoDB introduces an additional layer of complexity to customers’ IT spend. This week, we announced support for MongoDB on the CloudZero Platform.

As today’s enterprises shift to the cloud, Kubernetes has emerged as the de facto platform for running containerized microservices. And while Kubernetes operates as a single cluster, enterprises inevitably run their applications on a complex, often confusing, architecture of multiple clusters deployed to a hybrid of multiple cloud providers and private data centers. This approach creates a lot of problems. How do your services find each other? How do they communicate securely?

AWS re:Invent has come and gone. A great deal of fun was had along with many new insights shared. Here’s a recap from some ScienceLogicians who were in attendance.

Amazon Web Services is a major cloud services platform used by companies around the globe. Its cost-effectiveness and high agility has helped brands across all categories, verticals, and sizes scale their services quickly and efficiently. With many organizations now leveraging AWS resources to develop, build, and run business-critical applications in the cloud, it is important to track and monitor the performance of these services in real time to avoid unexpected issues.