Customer-facing applications request and process many types of sensitive data, such as API keys, credit card numbers, and email addresses. As your application scales in size and complexity, it becomes harder to keep track of this sensitive data moving across more services, increasing the risk of data leaks.
For any organization that stores, processes, or transmits cardholder data, monitoring can pose a particular set of challenges. The Payment Card Industry (PCI) Data Security Standard (DSS) dictates rigorous monitoring and data security requirements for the cardholder data environments (CDEs) of all merchants, service providers, and financial institutions.
To optimize its cloud investments, your organization needs internal stakeholders to act on shared knowledge about its cloud costs and cloud usage. But in practice, it’s difficult for organizations to gain a high degree of clarity about their cloud spending. The factors contributing to cost data are not normally visible to all stakeholders, and it’s often impossible to attribute costs to the teams, services, and applications that incurred them.
Today at Dash 2022, we announced new products and features that enable your teams to break down information silos, shift testing to the left, monitor cloud and application security, and more. Now, you can analyze cloud cost data alongside other telemetry, create synthetic tests for your mobile applications, and prevent malicious activity in your environment by blocking IPs directly from Datadog. We expanded Sensitive Data Scanner to include APM, RUM, and Events stream data.
Netdata just launched a Pandas collector. Pandas is a de-facto standard in reading and processing most types of structured data in Python so if you have some csv/json/xml data, either locally or via some HTTP endpoint, containing metrics you’d like to monitor, chances are you can now easily do this by leveraging the Pandas collector without having to develop your own custom collector as you might have in the past.
Cloud Service Providers (CSPs) offer an ever-expanding array of instance types, ensuring that for any given workload there exists the perfect hosting option that matches the exact needs of that app or business service. But with this expansion comes an ever-increasing challenge to match the workloads to the offerings – there are many things to consider.
Nowadays, the software development paradigm is based on containerizing applications to deploy on pods to let Kubernetes manage it. Containerized applications can then allow Kubernetes to manage its deployment, replication, high availability, metrics and other capabilities so that the application can focus on doing what it was designed to do. This technology is used for projects and by customers all over the globe.
