Systems | Development | Analytics | API | Testing

As organizations adopt specialized streaming technologies, real-time data is increasingly distributed across multiple domains, clouds, and locations. With the rapid evolution of streaming-dependent technologies, companies need the ability to pivot between providers almost instantly to spread risk and maintain performance (just look at the January DeepSeek-R1 LLM news).

Your application is built to deliver value. But without strong analytics, you may be leaving ROI on the table. Until now, embedding analytics features has been an afterthought, a luxury that’s hard to justify for your application.

One of the main challenges while building a software application is data storage. An easier way would be to store data in Excel and its likes, but they don’t really help with large amounts of data. When dealing with high volumes of data, a database is a better choice. You can either opt for a relational database such as Microsoft’s SQL Server, or a non-relational database such as MongoDB.

Neglecting data privacy in your business is like forgetting to lock your house doors and then leaving the country to enjoy your holidays. Sooner or later, someone will take advantage and break in. Similarly, if your business collects, stores, and analyzes customer data on a daily basis but neglects data privacy and security, you’re immediately exposed to several risks and large legal penalties. And in the process, you’ll experience financial loss and lose the trust of your customers.

The emergence and growing adoption of generative AI and the agreement to and implementation of the EU AI Act uncannily coincided. These two factors have catalyzed an AI renaissance within many enterprises. Yes, companies were already applying AI here and there across their organizations — but responding to the impact of these two exogenous forces required a whole new way of thinking and doing. All of a sudden, all eyes were on AI.

I host two podcasts, Software Engineering Daily and Software Huddle, and often appear as a guest on other shows. Promoting episodes—whether I’m hosting or featured—helps highlight the great conversations I have, but finding the time to craft a thoughtful LinkedIn post for each one is tough. Between hosting, work, and life, sitting down to craft a thoughtful LinkedIn post for every episode just doesn’t always happen.

We at Keboola believe that there can’t be too much efficiency in data engineering. Any data professional would surely agree, there is no such thing as good enough. And while better tools sometimes offer incremental steps, pushing the needle a little step at a time, a combination of tools can lead to impressive synergies. Keboola CLI and Cursor AI are exactly that—a combination that’s more than a sum of its parts.