Data Modeling in the Modern Data Stack

Data modeling is arguably the most impactful decision for a data team. It determines your architecture and the path that the whole team will follow. While this is not a new topic, the new tools and technology over the last decade have caused many to reconsider what’s best in a modern landscape. In this article, we will explore: <ol> <li>Why is data modeling (still) important?</li> <li>What are the common approaches?</li> <li>What things should you consider?</li> </ol> <h2>Why is data modeling (still) important?</h2> <ul> <li>Handling Different Data Sources: An effective data modeling strategy is essential for managing and integrating data from a variety of sources. Whether it’s databases, APIs, spreadsheets, or other data repositories, a robust data modeling approach ensures that information from different sources can be seamlessly harmonized, transformed, and integrated.</li> <li>Many Data Consumers: A lot of people use data. It’s like tidying up information so that everyone can get it easily. This makes the information right and dependable, and it helps different people cooperate and make smart decisions. As more people use data, data modeling makes sure everything stays neat and works well for everyone.</li> <li>Optimization (speed and cost): Optimization, which means making things better, is why data modeling is still important. When we organize data well using modeling, it becomes quicker to find and use, which saves time and resources. This also helps to save money on managing and storing data. So, even now, data modeling is really useful for making things work faster and cost less.</li> </ul>  <a href="https://towardsdev.com/data-modeling-in-the-modern-data-stack-d29be964b3a7">Read More</a>