Last year, we announced our plan to build a cloud-native Apache Flink® service to meet the growing demand for scalable and efficient stream processing solutions in the cloud. Today, we're thrilled to announce the general availability of Confluent Cloud for Apache Flink across all three major clouds. This means that you can now experience Apache Kafka® and Flink as a unified, enterprise-grade platform to connect and process your data in real time, wherever you need it.

Welcome back to Yellowfin Japan’s ‘How to?’ blog series! In our previous blog, we went through how to create big number and vertical column charts in Yellowfin, and the many different charting options available in Yellowfin Canvas. Before we re-visit our regular series, we want to share a shorter dashboard walkthrough.

If you are into programming, you might have used commands like cp, mv, cat, etc, to perform different operations using a text interface like bash or Windows PowerShell. This article discusses implementing command-line applications in Python with functionalities such as keyword arguments, flags, positional arguments, and mode selection. It also discusses how to implement the Linux head command in Python.

This blog post is about testing microservices and distributed systems with JMeter. It will focus on the principles of performance testing applications that are architected this way. We will not look at which JMeter samplers to use in order to generate a load against microservices or how to configure these samplers. This post will consider best practise and consideration in designing your performance testing when faced with these applications.

Many companies talk about cloud integration in a theoretical way. But cloud technologies aren’t theoretical. They’re a rapidly growing segment of technology that’s changing the way businesses operate. In the following article, we move from theory to practice so you can have a more realistic vision of what to expect when you move more of your on-site tech to the cloud.

Enterprises looking to increase productivity and optimize business processes are increasingly turning to artificial intelligence (AI). AI can meet these expectations—but only with the right enabling technology. Intelligent automation at scale across the organization can offer a strategic approach to incorporating AI into complex business processes.

The fascination with Artificial Intelligence (AI)-driven devices that streamline tasks continues to captivate individuals, driving the perpetual evolution of technology. Among the latest innovations captivating the tech industry’s imagination are chatbots. These virtual agents represent a culmination of advancements in artificial intelligence, marking a paradigm shift in how we interact with technology.

Amazon Web Services (AWS) ETL refers to a cloud-based set of tools and services that help extract data from different sources, make it usable, and store it in a way that makes it easy to analyze and make decisions based on it. AWS ETL tools offer a unique advantage for businesses seeking to streamline their data processes. These tools are efficient, scalable, and adaptable, making them ideal for a wide range of industries, from healthcare and finance to retail and beyond.

A Smartsheet report found that over 40% of workers spend at least a quarter of their workweek manually extracting data. Tax specialists in many organizations spend hours or even days sorting through piles of paper or PDF documents, looking for relevant information, and entering it into spreadsheets or databases. That’s a lot of time and money wasted on a tedious and error-prone process. Fortunately, there is a better way to handle tax form data extraction.