Quality assurance (QA) is a systematic process of determining whether a product or service meets specific requirements. A QA system is an indispensable part of the R&D process because, as its name suggests, it ensures the quality of the product.
This post introduces the QA framework adopted in developing the Milvus vector database, providing a guideline for contributing developers and users to participate in the process. It will also cover the major test modules in Milvus and methods and tools that can be leveraged to improve the efficiency of QA testings.

Ever looked for a comprehensive intro to Maven that is fun and entertaining at the same time? Then have a look at this brand-new episode of the “Marco Codes” YouTube channel: Maven Tutorial – Nice & Easy.
In this video, you’ll learn how to use Maven like a professional: installations, using the mvn wrapper, using Maven together with IDEs, and of course the Maven basics. From pom.xml concepts to running commands (clean install) to understanding Maven repositories and multi-module projects, by the end of it, there won’t be many questions left when it comes to Maven.

Abstract
Image classification can have many practical, valuable and life-saving benefits. The “Hello World” of image classification is often considered MNIST and, more recently, Fashion MNIST. This article will use Fashion MNIST and store the images in a SingleStore DB database. We’ll also build an image classification model using Keras and Tensorflow and store the prediction results in SingleStore DB. Finally, we’ll build a quick visual front-end to our database system using Streamlit that enables us to retrieve an image and determine if the model correctly identified it.
The SQL scripts, Python code and notebook files used in this article are available on GitHub. The notebook files are available in DBC, HTML and iPython formats.

Although many enterprises have deployed Kubernetes and containers, most also operate virtual machines. As a result, the two environments will likely co-exist for years, creating operational complexity and adding cost in time and infrastructure.
Without going into the pros and cons of one versus the other, it’s helpful to remember that each virtual machine or VM contains its instance of a full operating system and is intended to operate as if it were a standalone server—hence the name. By contrast, in a containerized environment, multiple containers share one instance of an operating system, almost always some flavor of Linux.

Presto is a distributed query engine that allows querying different data sources such as Kafka, MySQL, MongoDB, Oracle, Cassandra, Hive, etc. using SQL. It has the ability to analyze big data and query multiple data sources together.
In this article, we will discuss how Presto can be used to query Kafka topics. Below is the step-by-step process to set up Presto and Kafka, and connect them together. Here, I have considered MacOS, but similar setups can be done on any other system.

Microservices can help any organization achieve its goal of increasing agility by addressing critical factors such as improving team autonomy, reducing time to market, cost-effectively scaling for load, and avoiding complete outages of the applications. As organizations break their monolith applications into microservices, one of the major hurdles they encounter is identifying database dependencies.
Database sharing can be a complex and time-consuming challenge to solve. Databases do not allow you to define what is shared and what is not. While modifying a schema to better serve one microservice, you might inadvertently break how another microservice uses that same database.

Kubernetes is an open-source container orchestration tool developed by Google and is also known as K8s. It is used in managing the complete lifecycle of containerized applications. Kubernetes provides high availability, scalability, and predictability to the containerized application. It automates the deployment, management, and scaling of containerized applications. Kubernetes also supports automated rollout and rollbacks,  service discovery, storage orchestration, scaling, batch execution, and more. Kubernetes provides the cluster where containerized applications can be deployed. Kubernetes is not the only container orchestration tool, but various “Kubernetes Alternatives” are available in the market.
Before we talk about the “Alternatives to Kubernetes,” let’s explore the key components of Kubernetes. The Kubernetes cluster consists of at least one worker node where containerized applications are deployed and one master node or control plane which manages the worker nodes. The Control plane or master node consists of Kube-API server, etcd, Kube-scheduler, and Kube-controller-manager, whereas the worker node consists of Kubelet, Kube-Proxy, and Container Runtime. 

Microservices have become a buzzword when we talk about creating a scalable application. But is that enough? The simple answer is no. As with any software architecture decision, it has a trade-off and several challenges. Lucky for us Java developers, there is a combination of two tools to make our life easier: Microstream and MicroProfile. This article will cover combining Microstream and Wildfly to create a microservice application that is easily stable and ultra-fast.
Microservices With Wildfly
Microservices provide several challenges to software engineers, especially as a first step to facing distributed systems. But it does not mean that we’re alone. Indeed there are several tools to make our life easier in the Java world, especially MicroProfile. 

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