Systems | Development | Analytics | API | Testing

When building Node.js applications that interact with relational databases, you have a large variety of tools at your disposal to manage and execute queries. The three most common approaches — raw SQL, query builders, and Object-Relational Mappers (ORMs) — offer unique advantages and challenges, making it difficult to decide which is best. In this guide, we will compare the strengths, trade-offs, and use cases of all three approaches.

Data extraction is critical in 2025 as companies handle massive data volumes, expected to reach 175 zettabytes globally. Here's a quick breakdown of the best methods to extract, manage, and analyze data effectively.

RAG and Agentic AI shape how intelligent systems interact with data and users. RAG enhances LLMs by retrieving external information to improve accuracy and contextual relevance, while Agentic AI introduces autonomy, decision-making, and adaptability into AI-driven workflows. Agentic RAG combines the power of both, transforming RAG into a multi-step, autonomous, complex process that can self-improve.

In the rapidly evolving landscape of software development, QA teams never stop searching for means to optimize testing efficiency without losing precision. Playwright Model Context Protocol (MCP) has a new paradigm that is revolutionizing automated testing. Playwright MCP fills the gap between Large Language Models (LLMs) and test environments, naturalizing and simplifying QA automation. It is a paradigm shift in how testing is understood within the context of contemporary software development.

When running tests in a CI/CD pipeline, relying on external APIs can introduce instability, slow down execution, and even lead to failed builds due to rate limits or API downtime. Fortunately proxymock provides a solution by capturing API interactions and running a local mock server, enabling fully isolated and repeatable tests. In this blog, we’ll demonstrate how to integrate proxymock into a GitHub Actions CI pipeline using a demo app called outerspace-go.

In the part 1 of this series, we covered the fundamentals of caching (like fragment and Russian Doll caching). Before continuing, read that post here. This time, we’ll look at understanding Rails cache stores at a deeper level, so let’s jump in! Before we start part 2, let’s note that another way to make sure your Rails apps are ultra-fast is with Scout’s integrated monitoring and logging, so you can spot problems before your users do.

You know your business, you know your users, and you have some user needs and tasks. Great! But what's the next step? You first need to create a website to get your idea out there, right? Yes and no. There are a lot of tools that you can use to show people your idea, but only some of them are engaging. People want to interact with what you offer, which is why they need a website prototype. A prototype is the earliest form of a product, almost identical to the final product.

Volume testing is a type of software performance testing that evaluates a system’s capacity to process massive data volumes within a specific timeframe. It identifies bottlenecks, crashes, or inefficiencies under high data loads, ensuring performance, accuracy, and stability.

APIs power everything—from your favorite apps to critical business systems. But what happens when your API is subjected to large or unexpected traffic, security threats, or plunges under load? One weak link can destroy UX, destroy data or take down your service. API testing serves as your safety system beyond being a mandatory requirement. Faster development schedules and increased user demands during 2025 require organizations to make essential testing tool choices.

A data lakehouse is an innovative data architecture that blends the strengths of data lakes and data warehouses into a single, cohesive system. It retains the cost-effectiveness and flexibility of data lakes while incorporating the structured data management and performance optimization capabilities of data warehouses.