When we talk about technology, we should try to do it in the simplest possible terms. But while we hear many CEOs and other tech executives preach this advice, it’s rarely followed—and the workflow software space is no exception. Popular workflow lingo like hyperautomation, digital process automation, and intelligent automation all mean essentially the same thing: to digitize processes for efficiency, consistency, and compliance.

With the promise of auto-provisioning and self-healing, Kubernetes environments can be an attractive option for hosting your application platform. However, with increasing budget restrictions, the competitive cloud providers and offerings, and the need to do more with less, engineers are looking to get a handle on their resource utilization.

Testing plays an important role in software development. However, many projects still suffer from undetected bugs and integration problems.

Right tools can make a lot of difference, and in the world of software development, it’s absolutely true. VS Code, with its rich ecosystem of extensions, has become the go-to code editor for developers worldwide. As of 2025, here are the five must-have VS Code extensions that every developer should consider adding to their toolkit. These tools not only save time but also enhance our coding experience, ensuring that you can focus on what truly matters: building amazing software.

In the ever-expanding world of data-driven decision-making, data warehouses serve as the backbone for actionable insights. From seamless ETL (extract, transform, load)processes to efficient query optimization, building and managing a data warehouse requires thoughtful planning and execution. Based on my extensive experience in the ETL field, here are the best practices that mid-market companies should adopt for effective data warehousing.

In today’s data-driven world, Large-scale error log management is essential for maintaining system functionality. It can be quite difficult to pinpoint the underlying causes of problems and come up with workable solutions when you're working with hundreds of thousands of logs, each of which contains a substantial amount of data. Thankfully, automating this process using fine-tuned AI models—like those from OpenAI—makes it more productive and efficient.

Snowflake is one of the top cloud data warehouses. Regardless of the many documentations available, I have personally faced issues while carrying out Snowflake CDC (Change data capture). Therefore, I thought sharing everything a data practitioner should know about this before you start would be helpful. Let’s jump right into it!