A multi-cloud network is a cloud network that consists of more than one cloud services provider. A straightforward type of multi-cloud network involves multiple infrastructure as a service (IaaS) vendors. For example, you could have some of your cloud network’s servers and physical network provided by Amazon Web Services (AWS), but you’ve integrated that with your servers and physical networking that’s provided by Microsoft Azure.
In today’s data-rich world, data products that turn data assets into actionable insights are becoming increasingly valuable. Technologies that help users surface, process, organize, store, share, and act on data have defined a new generation of products. That’s why we sat down with Katie Bayes, a product engineer at IOPipe. Katie has deep experience designing data products, and her advice will prove valuable to any teams designing data products.
At Dash 2019, we are excited to share a number of new products and features on the Datadog platform. With the addition of Network Performance Monitoring, Real User Monitoring, support for collecting browser logs, and single-pane-of-glass visibility for serverless environments, Datadog now provides even broader coverage of the modern application stack, from frontend to backend.
Network Performance Monitoring is currently available in private beta. Request access here. Your applications and infrastructure components rely on one another in an increasingly complex fabric, regardless of whether you run a monolithic application or microservices, and whether you deploy to cloud infrastructure, private data centers, or both.
As your application grows in size and complexity, it becomes increasingly difficult to manage the number of logs it generates and the cost of ingesting, processing, and analyzing them. Organizations often have little control over fluctuations in the volume of logs generated—and the resulting costs of collecting them—so they are forced to limit the number of logs generated by their applications, or to pre-filter logs before sending them to their log management platform.
Security professionals have many tools in their toolbox. Some are physical in nature. (WireShark, Mimikatz, endpoint detection and response systems and SIEMs come to mind.) Others not so much. (These assets include critical thinking faculties, the ability to analyze complex processes, a willingness—some call it a need—to dig in and find the root cause of an issue and a passion to learn and keep learning.) One such tool that’s often overlooked is, communication.
Data teams are faced with the challenge of transforming raw data into analysis that is accurate and comprehensible. Ideally, the data modeling work they do will make it easy to answer crucial questions across multiple teams, and can be shared for collaboration without having to reinvent the wheel every time a new question pops up. However, the process of turning data into insights is not linear.
We know that data is a key driver of success in today data-driven world. In fact, according to Forrester, data and insight-driven businesses are growing at an average of more than 30% annually. However, becoming a data driven organization is not easy. Companies often struggle with speed in accessing and analyzing their data, as well with ensuring delivery of trustworthy data that is free of critical errors.