Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Announcing PCI-Compliant Log Management and APM from Datadog

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.

Gain visibility and control of your cloud spend with Datadog Cloud Cost Management

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.

Dash 2022: Guide to Datadog's newest announcements

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.

How to Reduce Costs With DevOps

As defined by Amazon Web Services, DevOps is the integration of cultural concepts, practices, methods, and tools which allow an organization to provide services and applications at high speed: advancing and improving their products at a much faster rate than those using traditional software process for infrastructure management and development. This allows organizations to serve clients more effectively and compete in the market.

Use Datadog Continuous Testing to release with confidence

Testing early and often in the development cycle is a must for ensuring that your application meets user expectations. Poor performance and errors can alienate users and prevent you from meeting crucial benchmarks and OKRs. Additionally, having to constantly implement fixes after new, under-tested features are added can fatigue developers and strain your resources, making your organization less nimble overall.

Leverage collaborative screen sharing with Datadog CoScreen

Remote collaboration tools have transformed how remote and hybrid teams work synchronously. But while the current popular chat forum and video conferencing solutions are inarguably helpful, few were created with software development and operations in mind. CoScreen is the only real-time collaboration tool designed specifically for remote and hybrid engineering teams that integrate both interactive screen sharing and video conferencing features.

Identify and redact sensitive data in APM, RUM, and Events stream with Sensitive Data Scanner

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.

AWS Vs. Azure Pricing: An Essential Guide For 2022

Microsoft's Azure and Amazon Web Services (AWS) are the two most popular cloud providers today. They both offer a variety of Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) solutions. In addition, you'll find products covering multiple computing areas, such as compute, storage, analytics, and networking. You can also deploy AWS or Azure services in the cloud, on-premises, or as a hybrid setup.

How you can use the Pandas Python collector to monitor weather 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.