Systems | Development | Analytics | API | Testing

If you have a legacy system with customized capabilities and valuable features but nearing end of life, refactoring the system is a potential choice for modernization. Insurance platform modernization usually involves one-to-one code migration, which can often be more costly and time consuming than expected and typically tends to miss some of the integration and data architecture modernization that is foundational for getting the full value of digitization.

Working with large volumes of data requires effective data management practices and tools, and two of the frequently used processes are data ingestion and ETL. Given the similarities between these two processes, non-technical people seek to understand what makes them different, often using search queries like “data ingestion vs ETL”.

When building your Laravel applications, you may sometimes need to use a NoSQL database to store and retrieve data. One popular choice is Amazon DynamoDB, a fully managed, serverless, and highly scalable NoSQL database service provided by Amazon Web Services (AWS). In this article, we'll take a brief look at DynamoDB. We'll then delve into how to use DynamoDB as a cache store in Laravel, and how to store Laravel models in DynamoDB using the baopham/laravel-dynamodb package.

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Learn the best practices to follow when implementing a test automation suite.

At the intersection of data management and business strategy lies the data steward. Tasked with safeguarding data integrity and enabling informed business intelligence, data stewards are fundamental to modern organizations. They ensure data is clean, compliant, and utilized effectively. Our exploration will detail the crucial role of data stewardship in navigating and leveraging an enterprise’s data landscape.

If you’re navigating the complexities of CRM and business automation, SFDC could be the game-changer you need. With SFDC, or Salesforce.com, businesses access a suite of tools for customer relationship management, marketing automation, and analytics to streamline operations. This article offers a focused look at how implementing SFDC strategies can elevate your data handling, improve customer engagement, and protect your information, all while bolstering your sales and marketing efforts.

At a time when AI is exploding in popularity and finding its way into nearly every facet of business operations, data has arguably never been more valuable. More recently, that value has been made clear by the emergence of AI-powered technologies like generative AI (GenAI) and the use of Large Language Models (LLMs).

The software testing landscape, while crucial for ensuring application quality, has grappled with limitations in traditional methods for decades. This article explores how Artificial Intelligence (AI) is emerging as a game-changer, addressing these challenges and augmenting the capabilities of testers. By combining advanced machine learning, deep learning, natural language processing, and other techniques, AI offers a powerful toolkit to propel software testing into a new era.

In the past, testing for non-functional requirements (NFR) was often neglected, done to the extent possible at the end of the life cycle. For modern development teams, that is no longer an option - to deliver high-quality applications, they have to implement a comprehensive testing strategy with proper allocation of resources to both functional and non-functional components.