A typical bit of feedback I have had during my time at Splunk is that the Splunk Machine Learning Toolkit (MLTK) looks nice and all, but how are we supposed to get started using it? Choosing the right technique, let alone the right algorithm can be a daunting task for those who are unfamiliar with machine learning (ML). We’ve been thinking long and hard about how we can help offer more prescriptive introductions into using ML at Splunk and I’m pleased to present our set of MLTK deep dives.

MongoDB is a cross-platform NoSQL database that uses JSON-like documents with optional schema to store data. It was designed for high availability, high performance for high-data persistance use cases, and automatic scaling. Of course, all with the right infrastructure in mind. It is usually a good choice for document-oriented use cases when you need quick prototyping or massive scale. With the massive scale comes massive traffic, though.

Jest is a JavaScript-based testing framework that lets you test both front-end and back-end applications. Jest is great for validation because it comes bundled with tools that make writing tests more manageable. While Jest is most often used for simple API testing scenarios and assertions, it can also be used for testing complex data structures. In this tutorial, we will go through how to use Jest to test nested API responses and hopefully have fun in the process.

Maximizing the value of streaming data requires carefully navigating operational tradeoffs when developing and managing cloud native applications. Organizations that are rapidly producing and processing high volumes of data — like Netflix, Salesforce, Shopify and even the United States Postal Service (USPS), are constantly applying and testing new methods to manage the complexity of data streaming in the cloud.

The Datadog platform has evolved to meet the needs of organizations that are investing in cloud-based solutions and modernizations. These organizations need professionals that are highly skilled and understand how to get the most out of the Datadog platform. With our suite of products, features, and tools in mind, we wanted to offer a path for individuals to demonstrate their knowledge of our best-in-class monitoring platform and their understanding of observability best practices.