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.

We’re excited to talk about our vision for Tableflow, which makes it push-button simple to take Apache Kafka® data and feed it directly into your data lake, warehouse, or analytics engine as Apache Iceberg® tables. Making operational data accessible to the analytical world is traditionally a complex, expensive, and brittle process and we believe we can do better to unify the operational and analytical estates.

We’re excited to share the latest and greatest features on Confluent Cloud, in our first launch of 2024. This Cloud Launch comes to you from Kafka Summit London, where we talked about the latest updates highlighted in our launch, including serverless Apache Flink®, some exciting pricing changes, updates to connectors, and more! We also shared our vision for a future offering, Tableflow.

The first time I learned about snapshot testing was in the context of front-end testing. It made a lot of sense. Writing an assertion-based test to check if a component was correctly rendered as HTML is tricky. And difficulty grows with the complexity of the output. Snapshot testing seemed a reasonable technique to get the job done, even with some caveats.

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.

Distributed architectures have become an integral part of modern digital landscape. With the proliferation of cloud computing, big data, and highly available systems, traditional monolithic architectures have given way to more distributed, scalable, and resilient designs. In this blog, we look at what makes an application distributed and how distributed applications work to bring about high availability, scalability, and resilience.

In this post, we'll add file upload capabilities to a Phoenix LiveView application and directly upload files to Amazon S3. Without further ado, let's get started!

In this blog post, explore the unique challenges of Continuous Deployment in native mobile apps, including the complexities of app store distribution and rollback limitations, and discover insights on navigating these hurdles while striving for efficient CI/CD workflows in mobile development.

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.

With the GA of Apache Spark stored procedures in BigQuery, users can extend their queries with Spark-based data processing via BigQuery APIs.