Learn the structure, syntax, and programming paradigm of the Java
platform and language. Start by mastering the essentials of object-oriented
programming on the Java platform, and progress incrementally to the
more-sophisticated syntax and libraries that you need to develop complex,
real-world Java applications.
Memory footprint and startup time are important performance metrics for
a Java virtual machine (JVM). The memory footprint becomes especially
important in the cloud environment since you pay for the memory your
application uses. This tutorial shows you how to use the shared classes feature in Eclipse OpenJ9 to reduce the memory footprint and improve your JVM startup time.
Servlet 4.0 fully integrates HTTP/2's server push technology, and also
enables runtime discovery of a servlet's mapping URL. With video
demonstrations and code examples, this hands-on tutorial gets you started with
HTTP/2 server push and the new HttpServletMapping interface in Java servlet
and JSF applications.
Eclipse MicroProfile has just delivered five new APIs for developing Java cloud-native microservices. Get the highlights of what's new in MicroProfile 1.3, with code examples that will have you up and running in no time.
In my last two posts in the C++17 STL series, I covered how to use std::optional. This wrapper type (also called "vocabulary type") is handy when you'd like to express that something is 'nullable' and might be 'empty.' For example, you can return std::nullopt to indicate that the code generated an error... but it this the best choice?
The confusion matrix is one of the most popular and widely used performance measurement techniques for classification models. While it is super easy to understand, its terminology can be a bit confusing.
Therefore, keeping the above premise under consideration, this article aims to clear the "fog" around this amazing model evaluation system.
There are many situations where we prefer using Amazon S3 as the destination for our date lakes, but increasingly we are also using GitHub as a data lake destination. While GitHub repositories do have some constraints when compared to Amazon S3, when it comes to specific types of big data projects it also has some significant advantages over Amazon S3. Providing us with a solution that can be checked out, forked, and version controlled, helping us stream the data we need across different applications.
Amazon S3 provides us with an industrial grade solution for streaming data in our data lakes. We are developing a growing number of AWS Lambda serverless apps for streaming data from common API sources into an Amazon S3 data lake. However, we are also developing a line of similar functions that stream the same data into GitHub repositories, providing more of a flexible alternative to developing real-time data lakes. Developing approaches to data storage that will allow developers to craft more precise, potentially distributed, and collaborative data lakes.