The open source revolution is back in full swing with the rise of Kubernetes. Flexibility and agility are the key factors to making the most of the cloud, multicloud, or hybrid cloud era. Kubernetes makes that easier by granting DevOps teams greater control across their infrastructure. But easier does not necessarily mean easy — there are still hurdles to overcome.
Despite decades of development, deep learning has been boosted by the increasing processing power of computers and the immense amount of data available, which has allowed it to obtain some achievements that were unthinkable years ago. Deep learning is at the heart of what we know today as artificial intelligence. However, like everything that is talked about a lot, deep learning has formed a series of myths that do not always obey reality.
Digital, network-connected systems are transforming every aspect of business — from your mission-critical workloads to your most rarely used applications. But the increases in scalability and cost efficiency come at a cost. Because every system is so reliant on network connectivity, unplanned downtime is becoming increasingly expensive.
Today we’ll talk more about the cost tracking for serverless architecture. Following the cost of serverless architecture utilization it might give you insights on how and what should you change or adapt to, to be able to reduce the cost. After all, we’re all always trying to improve our performances in every field while keeping the costs to a minimum (if possible).
In 2018, digital transformation ushered in a radical shift in how enterprises harness customer insights, technology capabilities, and rapid experimentation to drive revenue growth, profitability, and market leadership. Enterprises spent $1.3 trillion in 2018 on digital transformation technologies like public cloud platforms, microservices and containers, edge computing, machine learning, and artificial intelligence to improve customer experiences, business agility, and employee engagement.
If you’re a software developer, then you understand how vital application logging is in software development and a critical part of logging is something called logging levels. Log entries generally contain essential information—such as a timestamp, a message, and sometimes additional stuff like an exception’s stack trace. Those pieces of information are useful because they allow someone reading the log entry to understand how the application behaved in production.
The brand new Monitive platform is almost ready to be seen by the world, but just like any software development project, minor issues always surface that tend to push a release date back further and further. There’s always copy to be adjusted, links to be added, an icon that would improve clarity. Maybe a box is missing or the color of a button needs to change.
Monitoring, observability and analytics have become common phraseology in the Citrix world. Alongside every Citrix project, whether it’s a new deployment, expansion, upgrade, or cloud migration, what is essentially needed in the IT toolkit is analytics. Analytics means not just data, but insights obtained through continuous monitoring; periodic measurements of critical performance metrics that convey a lot about the health and availability of the Citrix Virtual Apps and Desktops session.
Culture is the way we do things together. It’s the secret sauce that results in happy, healthy teams that consistently meet their goals. It’s also the hardest thing to define, cultivate, and change in an organization. True cultural change requires more than creating and communicating policies. It takes collaboration, persistence, and experimentation.
Since all technology traffic analysers have evolved, and in order to answer the question of what the next step in this evolution is many experts suggest that the deep packet inspection is the point to which all will evolve. However, deep packet inspection is not a new concept; ISPs have been using it, not without controversy, for some years. Then why is it referred to as the next step?
