Today’s cyber landscape is littered with hackers trying to evade detection and defenders trying to improve detection. One aspect of this cyber landscape is the hardware devices that attackers plug into computer systems to gain access and perform malicious acts. In doing so, defenders build upon detection to find indicators of compromise in order to respond to this type of attack.
Scalable, nimble and efficient are terms commonly used to describe microservices, and as such, services are built to meet specific needs based on user features or application requests. However, when services need to communicate among one another, this can become somewhat convoluted and can lead to a significant amount of technical debt if not managed effectively. Target was faced with such a scenario in which it owned 40+ Spring Boot services and service-to-service communication was necessary to ensure service handoffs and SLAs were met. This post will walk through our implementation of Spring Feign Client, our learnings, and how Spring Feign Client has helped manage our inner-service communication while reducing the amount of development time.
Introducing matrixprofile-ts, a Python Library for Detecting Patterns and Anomalies in Massive Datasets
Time series data is everywhere. From finance to IT to marketing, many companies produce a myriad of metrics from which they hope to extract valuable insights. Within Target, our team collects hundreds of thousands of time series from across the business and monitors them for anomalous events. At the same time, we’re interested in discovering temporal patterns of behavior. How do milk sales vary throughout the week? Does our Kubernetes cluster follow a common auto-scaling pattern? This poses a significant challenge: Each of our metrics can be vastly different, whether through seasonality, trends or overall level.