Systems | Development | Analytics | API | Testing

Providing your analytics users the ability to get answers from their data is useful, but only if the solution can guide them to ask the right questions. The rising integration of artificial intelligence (AI) and machine learning (ML) in many business intelligence (BI) solutions has enabled new innovative approaches in combining natural language techniques with self-service analytics, resulting in helpful NLQ tools.

There are few things that can derail developer productivity and undermine your pipeline like a flaky test. Testing is the backbone of a good development process, ensuring that your code is as accurate and usable as possible. When these tests point towards faulty development, the impacts can be significant. This information is predicated on an assumption, however – the assumption that what the test says is accurate.

Most engineering teams have infrastructure monitoring nailed down—they are tracking uptime, latency, and error rates, and have set up alerting in places. But API issues don’t always start there. Infrastructure metrics don’t tell you how your API users experience your API. A critical integration may have been repeatedly facing failures due to invalid authentication tokens. A new version you have deployed might have introduced a subtle schema change that breaks older clients.

Data masking protects sensitive information by replacing real data with realistic, fictitious values. It’s an essential technique for securing data, reducing breach risks, and meeting compliance standards. Let’s explore the fundamentals of data masking, its importance, common methods, and some real-world examples of how it’s used.

JSON (JavaScript Object Notation) is a fundamental component of modern web development. Its simplicity and readability have made it a universal data interchange format, used across a wide range of industries and applications. The straightforward structure of JSON, which is both human-readable and machine-parseable, has contributed to its widespread adoption.

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team. This is the foundation of a multi-agent system—networks of agents, each with a specific role, collaborating to solve larger problems. When building a multi-agent system, you need a way to coordinate how agents interact.

Fivetran becomes the only fully managed platform that can move trusted, governed data in any direction, powering real-time decisions, AI, and business operations.

Learn the latest best practices for knowing which mobile devices to test on, plus our recommended devices to test on by region and business size.

Process automation has been around for decades, but the tools under this technology umbrella have multiplied over the years. Robotic process automation (RPA) was an early tool for handling simple, routine tasks, and it’s still powerful to have in your intelligent automation arsenal. But when technologies like intelligent document processing, business rules, and workflow orchestration entered the scene, they brought new capabilities to the process automation suite.

Are you leading the development or marketing of an app or website that involves protected health information (PHI)? If you have not heard the term before, PHI involves information related to an individual’s health status, healthcare, and healthcare payments. The Health Insurance Portability and Accountability Act (HIPAA) protects said individuals and their health-related information, which you must comply with before progressing further with your product or service.