Over 70% of global enterprises generate, store and use their data in hybrid environments. But while hybrid cloud adds flexibility, it also introduces complexity. This often leads to customers avoiding implementation of mixed virtualization and container platforms in order to prevent additional complexity and to address talent shortages, as each platform is intricate and requires specialized skills.

With over 6.1 billion smartphone users expected by 2028, how will your app be unique in a highly saturated and competitive environment? It is all about proper mobile app testing, or rather, the lack of it in most cases. Good usability and high-level user experience are the major factors in the current conditions of market competition. That is where tools like emulators and simulators are really useful.

Business leaders today face a critical challenge: How do you move from analyzing what happened to confidently predicting and shaping what comes next? Throughout 2024, we've enhanced Qlik Cloud's predictive AI capabilities to help organizations bridge this gap, enabling them to predict future outcomes, understand key drivers, and take informed action.

For years, Cloudera’s platform has helped the world’s most innovative organizations turn data into action. As the AI landscape evolves from experiments into strategic, enterprise-wide initiatives, it’s clear that our naming should reflect that shift. That’s why we’re moving from Cloudera Machine Learning to Cloudera AI.

Chaos testing, also known as chaos engineering, is one of the most-used methodology to test the resilience and reliability of systems by intentionally introducing controlled failures. Simply put, the goal is to identify weaknesses and improve a system’s ability to handle unexpected scenarios. Originating from Netflix’s famous Chaos Monkey tool, chaos testing has become a key practice in building robust distributed systems.

Hello, and welcome to part two of the BugSnag Beginners Series! We will cover the error details page and how it can be used to maximize error debugging in your BugSnag Dashboard. We’ll start by going over how error details heading information is organized, followed by what different pivots mean, how the status box works, where to find diagnostic data, and lastly how to create custom filters using your error’s metadata.

Apache Iceberg is transforming how organizations hanle data by solving key challenges in traditional data warehousing. It offers schema evolution without downtime, automated partitioning, ACID compliance, and time travel for historical data access. Its open table format separates storage and compute, enabling scalability, flexibility, and cost efficiency.

Software development is a dynamic field that needs reliable and effective testing practices. Artificial Intelligence (AI) is poised to revolutionize software testing services in this dynamic environment. As 2025 draws near, AI is set to transform quality assurance (QA) by automating processes, increasing accuracy, and boosting software quality in general. In this blog article, the leading AI developments in software testing for 2025 are examined.

Artificial intelligence (AI) has become an integral part of modern software, driving innovation across industries from healthcare to finance. However, as AI systems increasingly influence critical decisions, concerns around bias, fairness, and ethical implications have come to the forefront. Quality Assurance (QA) professionals are uniquely positioned to address these challenges, ensuring that AI systems are not only functional but also equitable and ethical.

Today, we are excited to announce the general availability of Claude 3.5 Sonnet as the first Anthropic foundation model available in Snowflake Cortex AI. Customers can now access the most intelligent model in the Claude model family from Anthropic using familiar SQL, Python and REST API interfaces, within the Snowflake security perimeter.