Cloud Native

Cloud Native Data Science: Best Practices

Following the Cloud Native best practices of immutability, automation and provenance will serve you well in a CNDS project. But working with data brings its own subtle challenges around these themes.

Cloud Native Data Science: Technology

Technology choices in data-driven products are, as you would expect, largely directed by the type and amount of data. The first and most crucial decision to make is whether the data will be processed in a batch or streaming fashion.

Cloud Native Data Science: Strategy

Data Science has become an important part of any business because it provides a competitive advantage. Very early on, Amazon’s data on book purchases allowed them to deliver personalised recommendations whilst customers were browsing their site. Their main competitor in the US at the time was Borders, who mainly operated in physical stores. This physicality prevented them from seamlessly providing customers with personalised recommendations [1]. This example highlights how strategic business decisions and data science are inextricably linked.

Introduction to Monitoring Microservices with Prometheus

https://prometheus.io is an open source time series database that focuses on capturing measurements and exposing them via an API. I love Prometheus because it it so simple; it’s minimalism is its greatest feature. It achieves this by pulling metrics from instrumented applications, not pulling like many of its competitors. In other words Prometheus “scrapes” the metrics from the application.

This means that it works very well in a distributed, cloud-native environment. All of the services are unburdened by load on the monitoring system. This has knock on effects meaning that HA is supported through simple duplication and scaling is supported through segmentation.

Logging vs Tracing vs Monitoring

What do you mean by monitoring? Why do you need it? What are the real needs and are you monitoring them? Ask yourself these questions. Can you answer them? If not, you’re probably doing monitoring wrong.

This post asks the basic question. What is monitoring? How does it compare to logging and tracing? Let’s find out.

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