Logging is a crucial practice that helps developers monitor, debug, and maintain their applications. Python, a versatile and widely used programming language, provides a comprehensive logging module that makes it easy to integrate logging into your applications. This post introduces Python logging, covering the basics, various logging levels, practical implementations, and more.

According to a survey by Markets and Markets, the EdTech sector is seeing an unparalleled upswing, with the worldwide market size expected to reach an astounding $232.9 billion by 2027 at a CAGR of 13.2% over the period 2022–2027. This extraordinary expansion highlights the critical need for Edtech software testing services as well as a revolutionary change in how we study and access educational information.

In today's data-driven world, Power Query has become an indispensable tool for professionals dealing with data analysis and manipulation. However, it's crucial to understand the limitations of Power Query to maximize its potential and complement it with more advanced tools when necessary.

The pace of change in the world is rapidly accelerating. Technology companies must prioritize speed to market to stay ahead of this curve, but speed can sometimes come at the expense of risk. The recent CrowdStrike incident is a sobering reminder that even the largest and most capable organizations are not immune to errors that can lead to significant downstream impacts.

API governance sets the rules and practices to manage APIs efficiently, ensuring they remain discoverable, consistent, and secure. It’s crucial for businesses aiming to control API sprawl and enhance collaboration. This guide explores API governance, its role in effective API management, its benefits, and best practices for successful implementation.

An API model acts as a blueprint for building scalable and maintainable software interfaces. In this article, you’ll discover how to design, implement, and validate a robust API model, including the use of AI models. We’ll cover key concepts like API modeling, essential components, user identification, and best practices.

In today’s regulatory environment, organizations are compelled to adopt stringent security measures to mitigate the risk of PII (Personally Identifiable Information) data breaches in their production environments. However, the pressure to provision more test data in non-production environments, often sourced from production data, has introduced new vulnerabilities.

Cloudera’s data-in-motion architecture is a comprehensive set of scalable, modular, re-composable capabilities that help organizations deliver smart automation and real-time data products with maximum efficiency while remaining agile to meet changing business needs. In this blog, we will examine the “why” behind streaming data and review some high-level guidelines for how organizations should build their data-in-motion architecture of the future.

Modern data platforms enable enterprises to extract valuable business insights from data, sourced from various origins. Data engineers, data scientists, and other data practitioners utilize both data streaming and batch processing frameworks as a means to provide these insights. While batch processes work on historical data, stream processing extracts insights in real time, enabling businesses to react faster with respect to changing events.

We are proud to announce the release of Apache Kafka 3.8.0. This release contains many new features and improvements. This blog post will highlight some of the more prominent features. For a full list of changes, be sure to check the release notes. See the Upgrading to 3.8.0 from any version 0.8.x through 3.7.x section in the documentation for the list of notable changes and detailed upgrade steps. In a previous release, 3.6, Tiered Storage was released as early access feature.