Systems | Development | Analytics | API | Testing

As enterprises double down on AI, there’s a hard truth many leaders are starting to face — they’ve invested in the promise of AI, but they can’t always trust the data behind the predictions. Whether you're training a model, building RAG pipelines, or scaling intelligent automation, AI outcomes are only as reliable as the data feeding them. Yet most organizations still can’t answer a critical question with confidence: Is our data truly AI-ready?

In our previous article, we explored the basics of monitoring Ecto with AppSignal, covering everything from initial setup to tracking key metrics such as query execution time and resource consumption. We even set up custom instrumentation for database connection pools to gain deeper insights into our application's performance. However, setting up monitoring is just the first step toward maintaining a healthy, high-performing Elixir application.

Fintech had a busy year in 2024. Global companies like HDFC, SBI, Paytm, and Google are eliminating the need for traditional financial intermediaries and utilising the best fintech software development companies to provide customers with almost anything, including banking, insurance, credit, mortgages, and more. The invention that gave rise to financial technology, or Fintech, seemed like science fiction even ten years ago.

Confluent has established itself as a leader in event streaming, providing not only a robust platform but also a rich portfolio of pre-built connectors. These connectors act as bridges, effortlessly channeling data between a multitude of systems, from databases and applications to cloud services. This extensive portfolio empowers users to weave together their data landscapes with remarkable ease and flexibility.

Three in 4 programmers have tried artificial intelligence (AI). This factoid comes from a recent Wired survey on the habits of engineers with respect to AI tooling like GitHub Copilot. Though Wired used a pool of only around 700 engineers, Gartner’s prediction from a year ago was that 75% of enterprise software engineers would use AI by 2028. To many of us, it’s starting to feel like that’s already happened.

As a current HubSpot user and veteran of a HubSpot partner agency, I know first-hand how powerful HubSpot is. And they’ve built a lot of great reporting enhancements into the platform over the last few years. And yet – we still hear that even the best HubSpot power users run into limitations (in functionality, pricing, or both).

In the world of AI, GPUs have become the undisputed workhorses of innovation. From training deep learning models to accelerating agentic workflows, digital twins, and scientific simulations, these powerful accelerators are indispensable. However, the immense computational power of GPUs comes with a significant investment.

The API landscape is evolving rapidly with some protocols rising in popularity while others fade away. The journey that started with SOAP has now evolved into other protocols like REST, GraphQL, gRPC, AsyncAPIs, and more. With the emergence of large language models (LLMs), we are now in the era of AI agent/assistant integrations with APIs. LLMs are power utilities. However, they operate without contextual awareness.

DreamFactory has transformed from a basic API automation tool into a Data AI Gateway, addressing modern enterprise challenges like managing APIs, integrating data, and ensuring security. Here's why this evolution is important: API Management Simplified: DreamFactory generates secure REST APIs for databases in just 5 minutes, saving time and reducing development costs by up to $201,783 annually.

As data privacy regulations grow more complex, particularly in the European Union, organizations are under increasing pressure to ensure their data remains within jurisdictional boundaries. Many EU-based businesses, especially those in regulated industries, are legally required to store and process data within the EU to comply with frameworks like the General Data Protection Regulation (GDPR).