Systems | Development | Analytics | API | Testing

Ever since the release of ChatGPT in November 2022, organizations have been trying to find new and innovative ways to leverage gen AI to drive organizational growth. LLM capabilities like contextual understanding and response to natural language prompts enable the development of applications like automated AI chatbots, smart call center apps, or for financial services.

In the ever-evolving web design and development world, having the right tools at your disposal can make all the difference. One such tool that has been making waves in the industry is InVision. With over a million users worldwide, InVision has become a go-to platform for web professionals looking to streamline their design process and create stunning, interactive prototypes.

We are in a time where new technologies are being introduced rapidly in the market. This means for any business to stay competitive, it’s important to stay adaptable to change. You have to keep informed of the trends shaping the future to keep thriving. One of the most important frontiers for innovation to keep watching is the software development sector. By understanding the currents and undercurrents in this area, you can get a wider view of where the world is heading.

Building a data-driven company starts with having a data analytics strategy. An effective data analytics strategy relies on three key components: people, processes, and data infrastructure. This ensures that your analytics strategy is actionable and accessible to everyone who needs it to make decisions. In this post, we’re sharing 12 tips to help you develop an effective data analytics strategy.

The microapp trend is on the rise! In the approximately two years since joining the DreamFactory team, I’d estimate I’ve conversed with more than one thousand companies about their API-based projects. These conversations provide a great opportunity to peer inside the IT operations of organizations large and small, not to mention pick up on emerging technology trends.

Cybersecurity is one of the most prominent and pressing concerns for businesses today. As developers move more of their web applications to a microservices architecture, data is shared by web service APIs now more than ever. Industry analysts predicted that API attacks would become the most common attack vector by the end of this year. Unfortunately, that prediction has come true.

Erlang is renowned for its remarkable fault tolerance and high concurrency. Erlang's scheduler efficiently handles many lightweight processes. The scheduler plays a crucial role in managing processes, concurrency, and system resources, efficiently coordinating these elements to help Erlang maintain fault tolerance and support high levels of concurrency in its applications. This post will dissect some of the scheduler's key components and shed light on how it works internally. Let's get started!

The ability to record and see everything happening across your web applications is essential when building resilient and highly available systems. All of your events—from application logs to errors to user behavior—contain data that could be useful to you and your team. When you have a central place to access all this information, finding issues and their root causes becomes easier because you have the data at your fingertips.

Metadata governance is the systematic approach to managing metadata, or data about data, to ensure it is accurate, consistently formatted, and securely stored. The main goal of metadata governance is to improve data quality, ensure data privacy, and streamline metadata management. As metadata provides the necessary details about data, such as when it was created, by whom, and its content or purpose, it becomes easier for employees to find, understand, and trust the data they use.

No matter their size, all organizations rely heavily on the data they collect and manage. This data ranges from customer information to sales records, employee performance, and more. However, if this data is inaccurate, outdated, or incomplete, it becomes more of a liability than an asset, making it more important to measure its health. To do so, they need data quality metrics relevant to their specific needs.