The pandemic upended business for many or at the very least cast a grim shade of uncertainty, so, as many took to working from home, they also were commissioned with cutting waste. Among the biggest sources of misspend in 2020 – cloud services. And remote work may have actually spurred the problem, as organizations migrate more applications to the cloud to support these workers.
Operating in today’s digital economy often involves dealing with an extensive network of third-party providers and partners. Common types of partner networks include affiliates, vendors, suppliers, marketing platforms, and payment gateway providers. Partner networks involve tracking and analyzing data from multiple providers, each of which creates thousands of metrics and billions of events each day.
Hybrid Fiber Coaxial (HFC) networks are inherently agile and able to adapt to demand spikes. This significant characteristic was most recently demonstrated by their success in handling pandemic-induced traffic surges.
The move to cloud computing has been a no-brainer for many enterprise companies. But cloud computing is an expense that, unlike many other operating costs, is largely variable. Many companies — including the fastest-growing startups, largest enterprises, and leading government agencies — choose AWS to help them streamline fragmented processes, reduce costs, become more agile, and innovate faster.
In the digital age, the speed and performance of apps and websites have a huge impact on the customer experience. To ensure a high level of quality, Application Performance Monitoring (APM) refers to the process of tracking the performance and availability of software systems. Let’s look at what Application Performance Monitoring is, how AI and machine learning are being applied to stay ahead of the competition, and several real-world use cases.
Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks, in a competitive and unforgiving market with minimal time to resolution and high costs for errors. With the ongoing growth in operational complexities, effectively managing radio networks, current and legacy core networks, services, and transport and IT operations is becoming a radical challenge.
5G is in the process of transforming communications technology, enabling never-before-seen data transfer speeds and high-performance remote computing capabilities. As a cloud-native application, 5G provides advantages in terms of speed, agility, efficiency and robustness.
Telecom companies monitor their network using a variety of monitoring tools. There are separate fault management and performance management platforms for different areas of the network (core, RAN, etc.), and infrastructure is monitored separately. Although these solutions monitor network functions and logic – something that would seem to make sense — in practice this strategy fails to produce accurate and effective monitoring or reduce time to detection of service experience issues.
It could be argued that consumer broadband networks have historically been poor neighbours of business networks, with CSPs investing more funds in providing better SLAs to their higher paying business customers. But like it did for many of our pre-set ideas, the pandemic turned the tables around for broadband priority. Forced work from home policies, remote learning, and quarantines have effectively turned consumer broadband into business/educational/health broadband services for many.
We are often asked what’s the difference between Anodot and Datadog. Since both platforms monitor data at scale, using machine learning to detect anomalies and incidents, the differentiation might be unclear. So we’re using the real estate here to quickly clarify what each platform is built for, and why – despite some overlaps in features – these are two fundamentally different creatures.
