Data is at the core of every business decision, but how we handle it has changed dramatically in recent years. Traditional ETL (Extract, Transform, Load) tools like Informatica and Talend have served businesses well for a long time, helping to move and process data from one system to another. But as data needs grow and evolve, companies are finding that these legacy tools are struggling to keep up.
In the modern world, data is frequently likened to a precious resource, akin to a treasure chest waiting to be unlocked. Just as raw materials must be crafted into finished goods before they can be used, raw data needs to be meticulously processed and refined to reveal its true value. This is where data engineering comes into play. While data science focuses on extracting insights and knowledge from data, data engineering is about constructing and maintaining the systems that make this extraction possible.
