AWS Retires Documentation from Github

Weekly Product Updates #23-06: Kotlin, Helidon, Godot, Appium and Hibernate Search

Weekly Product Updates #23-05: Linux Kernel, Libreboot, Spring Cloud, Spring Shell and Quarkus

AlamaLinux is not going to be bug-for-bug compatible with RHEL

AlmaLinux is not going to be bug-for-bug compatible with RHEL

SUSE forks RHEL code to preserve choice in Enterprise Linux

Wing Cloud Raises $20Million in Seed Funding to Build Winglang

Weekly Product Updates #23.01: Bodhi Linux, Fedora, Elastic, React, RHEL and more.

Weekly Product Updates #23-03: Milvus, Quickwit, Eclipse, XWiki and PHPStan

Apache Pekko - An Akka Fork to build powerful reactive, concurrent, and distributed applications

Weekly Product Updates #23-07: Laravel Herd, Consul, Prometheus, Electron, Strapi and FastApi

Cassandra is a highly scalable and distributed NoSQL database known for its ability to handle large volumes of data across multiple nodes. It is designed to provide high availability and fault tolerance, making it a popular choice for applications that require real-time data processing and high read and write throughput. In this article, we will explore some common use cases for Cassandra and learn how to perform CRUD (Create, Read, Update, Delete) operations in Python using the Cassandra driver.

Cassandra: Use Cases and Python CRUD Operations

Apache Cassandra was designed by Facebook and was open-sourced in July 2008. It is regarded as perfect choice when the users demand scalability and high availability without any impact towards performance. Apache Cassandra is highly scalable, high-performance distributed database designed to handle large voluminous amounts of data across many commodity servers with no failure.

Introduction to Apache Cassandra

Lucene and Solr are most popular and widely used search engine. It indexes the content and delivers the search result faster. It has all capabilities of NoSQL database. This article describes about its pros and cons.

Lucene / Solr as NoSQL database

Supercharge Your Python Application: Redis as a High-Performance Cache

Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. It is pre-loaded with user authentication, content administration, site maps, RSS feeds and many more tasks. Security features provided are cross site scripting (XSS) protection, cross site request forgery protection, SQL injection protection, click-jacking protection, host header validation, session security and so on. It also provides in built caching framework.

Getting Started With Django Python Web Framework 

Exploring Playwright: A Comprehensive Guide to Web Testing and Automation

Most of the projects will have a requirement of sending and receiving mails. We have mentioned about GreenMail - Email Test Framework, in our previous article about API based SMTP testing. In this article, we discuss about MailHog - Web and API based SMTP testing. You send out a mail from your code and you can check it via web visually and also via API. Those who do API testing can check via API. Developers may want to visually verify the format of the mail. MailHog is a best bet for SMTP testing.

MailHog - Web and API based SMTP testing

In any project there will be a need to send mail out to users. It could be an alert mail, forget password or authentication related mail. Mail is the default communication between the software and the users. As a developer, we can write code, to send out a mail but we need to make sure whether it got successfully received and how the body of mail, Is it the same like what we have sent. GreenMail is a Email test framework which helps to send and receive mails. It is a test framework which supports SMTP, POP3, IMAP including SSL. 

GreenMail - Email Test Framework in Java

In this article, we will learn about how to create Bar Chart, Area Chart, Bubble Chart, Doughnut and Pie Charts, Line Chart, Mixed Chart Types, Polar Area Chart, Radar Chart, Scatter Chart, Cartesian Axes, Category Axis, Logarithmic Axis, Radial Axes, Labeling Axes, Styling using ChartJS.

ChartJs Visualization Library - A Detailed Walkthrough

Data Visualization Using Tremor React Library

In the world of data visualization, charts play a pivotal role in conveying information in a visually appealing and easily understandable manner. Charting libraries simplify the process of creating various types of charts, enabling developers to showcase data trends, comparisons, and distributions effectively. 

Create Basic Charts with ChartJS

Educate and Empower software professionals in emerging technologies, news and best practices.

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AWS Retires Documentation from Github

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