In the space of APIs for consuming up-to-date data (say, events or state available within an hour of occurring) many API paradigms exist. There are file- or object-based paradigms, e.g., S3 access. There’s database access, e.g., direct Snowflake access. Last, we have decoupled client-server APIs, e.g., REST APIs, gRPC, webhooks, and streaming APIs.

Django is designed to be secure by default, which means that its got a number of featured out of the box to protect against common Web vulnerabilities, including SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF). Its extensive documentation, active community, and strong ecosystem of reusable apps and libraries make it a common choice for developers looking to create scalable and maintainable Web applications.

Generative AI has received the lion’s share of the press. With good reason—it’s revolutionizing the way we do work and do business. But it’s not the only game in town. Predictive AI also places a role across enterprise use cases like demand forecasting, maintenance, and customer experience. This blog will discuss these two types of AI: generative AI and predictive AI.

Handwriting code for database APIs can be a time consuming and expensive process that often exposes organizations to a variety of risks relating to quality, consistency, and developer effort. DreamFactory specialises in REST API generation for a range of databases that gives you the ability to generate a secure, fully documented API in minutes.

Organizations must migrate to SAP S/4HANA by December 2027. Using technologies beyond their End of Life (EOL) can lead to operational disruptions, increased maintenance costs, costly support services, security vulnerabilities, and compliance failures, ultimately resulting in higher expenses and diminished customer experiences. This short timeframe requires immediate planning.

As the wave of AI sweeps through the technology landscape, many have hopped on board. Interestingly enough, and often overlooked, is that many AI capabilities are served through APIs. Fancy user interfaces integrate with the actual mechanisms where the magic happens: the APIs. So, when generating revenue through AI platforms, the APIs drive the revenue.

Using BigQuery DataFrames with CARTO visualization tools can help Python users access cloud-native analytics and generative AI services.

Automated data governance is a relatively new concept that is fundamentally altering data governance practices. Traditionally, organizations have relied on manual processes to ensure effective data governance. This approach has given governance a reputation as a restrictive discipline. But, as organizations increasingly adopt automation in their governance processes, this perception is changing.

At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.

Welcome to the ultimate resource for mastering load testing! Whether you’re a non-technical business owner, a software engineer, a student, a product owner, a member of a web agency, or a DevOps professional, this article will show you how to approach load testing challenges head on. We’ll progress from the fundamentals to more in-depth strategies, all in a relaxed and easy-to-understand manner. Are you ready to ensure that your website runs smoothly even during peak traffic periods?