Systems | Development | Analytics | API | Testing

Driving Data, Delivering Value: Data Leaders to Watch in 2023

The Chief Data Officer is arguably one of the most important roles at a company, particularly those that aspire to be data-driven. CDO appointments and the elevation of data leaders have accelerated in recent years, and the role has morphed as perceptions of data have evolved. Responsibilities span strategy and execution, people and processes, and the technology needed to deliver on the promise of data.

NeoLoad and ChatGPT

Artificial Intelligence (AI) is coming, whether we like it or not. It is likely to transform every aspect of what it means to be human and represents an existential opportunity – or potential threat – to everyone living and yet to be born. Hyperbole aside, ChatGPT software intelligence has been a hot news topic of late. While it has some limitations, to many of us it’s the first real glimpse of AI’s capabilities.

Best data modeling methods for data and analytics engineers

Recently, I published a blog on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

Top 6 Python ETL Tools for 2023

Extract, transform, load (ETL) is a critical component of data warehousing, as it enables efficient data transfer between systems. In the current scenario, Python is considered the most popular language for ETL. There are numerous Python-based ETL tools available in the market, which can be used to define data warehouse workflows. However, choosing the right ETL tool or your needs can be a daunting task.

10 API Developer Mistakes to Avoid

With low-code tools on the rise, building an Application Programming Interface (API) is simpler than ever. Given the ease of development, it is easy to overlook potential problems. Taking a bit more time in the design phase can ensure the API is truly useful, secure, scalable, and stable. Here, we’ll discuss the top ten most common API development mistakes to avoid.