Systems | Development | Analytics | API | Testing

Python is a powerful and versatile programming language. However, like any language, it comes with its own set of challenges. One common issue developers encounter is the KeyError exception. In this tutorial, we’ll delve into what a KeyError is, explore common scenarios where it occurs, and provide practical solutions and best practices to handle and prevent this error.

According to a recent survey by AppDirect, half of IT decision-makers are losing sleep over security. It's no surprise, given that 45% of respondents have experienced a security breach in the last year. The threat of vulnerabilities and cyber security attacks is on the rise, and the impact of an incident can be devastating. Having the right DevSecOps strategy helps, but getting the right toolchain in place is crucial.

The benefits of API mocking extend beyond just speeding up development. It enables developers to work independently of external APIs, eliminating delays caused by dependencies that may be out of their control. This independence is crucial for maintaining workflow efficiency, especially in complex projects involving multiple APIs. API mocking is invaluable for testing edge cases and error scenarios that might be difficult to reproduce with a live API.

Effectively communicating insights derived from data is a cornerstone of successful decision-making in today's landscape. Yet, many businesses struggle to translate complex analytics into compelling narratives that resonate with diverse audiences. The ability to create engaging and informative presentations directly from data is a critical requirement for modern embedded analytics platforms.

As hard as it may be to believe, we are nearly a quarter of the way through the 21st century. Yet many businesses still rely on 20th century processes and technology to complete their financial planning and analysis tasks. This reliance on outdated methods can lead to inaccurate forecasts, missed opportunities, and a competitive disadvantage.

In this blog, we will walk you through the essentials of setting up Cucumber for your project. You will learn about the prerequisites, installation steps, and configuration needed to get Cucumber up and running. This guide will provide clear instructions for various environments, ensuring you have everything you need to start writing your first Cucumber tests.

Not too long ago, businesses tended to downplay the importance of integrating analytics into their operational strategies and embracing digital big data. The formidable challenges of hefty budgets and a need for more qualified digital transformation professionals were major barriers, deterring companies from fully engaging with the big data era.

Across industries worldwide, businesses are turning to ERP to automate repetitive tasks, enjoy easy scalability, stay flexible, and derive insights using a single source of truth. According to Statista, Enterprise Resource Planning (ERP) software market revenue will reach USD 53.15 billion this year. If your organization implements an ERP system, you’ll find that data migration is one of the most critical components of this process.

Over the years, Amazon DynamoDB has grown into a feature-rich NoSQL database that has deep integrations with various services such as Amazon S3 and AWS Lambda. As businesses increasingly depend on data for decision-making, it is common to use data residing in DynamoDB to contextualize or even drive events at a granular level (as opposed to bulk or batch).