We’re seeing a massive shift in how companies build their software. More and more, companies are building—or are rapidly transitioning—their applications to a microservice architecture. The monolithic application is giving way to the rise of microservices. With an application segmented into dozens (or hundreds!) of microservices, monitoring and consolidated logging become imperative.
While monitoring is an important part of any robust application deployment, it can also seem overwhelming to get a full application performance monitoring (APM) stack deployed. In this post, we’ll see how operating a Kubernetes environment using the open-source Kong Ingress Controller can simplify this seemingly daunting task! You’ll learn how to use Prometheus and Grafana on Kubernetes Ingress to simplify APM setup.
Last week, we shared information on BigQuery APIs and how to use them, along with another blog on workload management best practices. This blog focuses on effectively monitoring BigQuery usage and related metrics to operationalize workload management we discussed so far.
Businesses are flooded with constantly changing thresholds brought on by seasonality, special promotions and changes in consumer habits. Manual monitoring with static thresholds can’t account for events that do not occur in a regularly timed pattern. That’s why historical context of influencing events is critical in preventing false positives, wasted resources and disappointed customers.
The global pandemic has changed B2C markets in many ways. In the U.S. market alone in 2020, consumers spent more than $860 billion with online retailers, driving up sales by 44% over the previous year.eCommerce sales are likely to remain high long after the pandemic subsides, as people have grown accustomed to the convenience of ordering online and having their goods – even groceries – delivered to their door.
