Systems | Development | Analytics | API | Testing

After a decade immersed in the world of ETL (Extract, Transform, Load), I've witnessed firsthand the evolution of data ingestion. What was once a relatively straightforward process has become a complex, critical component of modern data pipelines. In the recent years, with the explosion of data sources and the ever-increasing demand for real-time insights, mastering data ingestion best practices in data engineering is paramount.

As developers, we know how essential APIs have become in building modern software systems, from internal services to public-facing products at scale. We spend countless hours crafting elegant code, meticulously designing interfaces, and ensuring everything should work flawlessly. And, we must ship these services fast to stay competitive. But what happens after deployment? In reality, even the most beautifully crafted API can fall flat without deep insight into its real-world usage.

Have you ever tried to ship a software update smoothly but suddenly encountered a few bugs that slowed down your entire DevOps cycle? Forgetting about these small issues can quickly turn them into big problems— time, money, and user trust. Software teams need fast, reliable testing, and manual methods can be inadequate. That’s why test automation has become a necessity, especially in high-velocity DevOps settings.

Software's nature is complex and disparate, and there isn't a one-size-fits-all way of locating faults. Different testing levels are done to catch bugs and render a hassle-free user experience. Some of the most basic yet essential tests include unit and system tests, each one of them crucial to the creation of software.

Imagine you are designing a new mobile app that will help global users in managing their schedules. You have spent a lot of time designing the features and polishing the design, but you are not sure how intuitive it really is. Jillian Heller Foreman , Research Manager, Usability,

In the dynamic world of software development, ensuring that applications remain stable and functional as they evolve is crucial. This is where regression testing plays a vital role, especially when employing powerful test automation tools like Playwright. For software developers and QA professionals, creating a detailed test plan for regression testing using Playwright can significantly enhance the efficiency and reliability of testing processes.

In today’s digital economy, businesses aren’t just competing on products and services—they’re competing on insights and decisions. The ability to deliver real-time, contextual analytics within applications and portals isn’t just a nice to have; it’s a critical advantage. Your users expect instant access to insights without switching between tools, hunting for reports, or waiting for analysts to provide answers. This is where embedded AI-powered analytics comes in.

Every article I read highlights the need for rapid adoption of AI, with emphasis on agentic and generative AI redefining the boundaries of data architectures. As organizations expand automation, personalization and real-time decision-making, they face a fundamental challenge: data gravity – especially to meet the demands of AI reasoning inference workloads – is pulling AI workloads toward storage, but disaggregated architectures struggle to keep pace with the need for rapid data access.

The era of AI experimentation is over. Organizations want to see ROI. And they will—as long as they understand that the competitive edge isn’t in AI itself. With AI evolving rapidly, businesses need a clear strategy that cuts through the noise and generates ROI. This key strategy is to embed AI into core business processes. This post will cover five enterprise AI trends for the new era of AI and why process is the key to ROI. The most talked-about trend today is agentic AI.

Dialyzer (DIscrepancy AnaLYZer for ERlang programs) is a powerful static analysis tool that helps developers identify potential issues in their Elixir code without executing it. It excels at finding type mismatches, unreachable code, and unnecessary functions through sophisticated flow analysis. In part one of this two-part series, we'll first get to grips with the basics of Dialyzer. In part two, we'll examine more advanced use cases.