Data applications, encompassing tools like recommendation engines and customized reporting tools, provide interactive insights and actions beyond traditional embedded analytics. While embedded analytics integrate exploratory tools into existing applications, data applications focus on data explanation by offering dynamic, purpose-built user experiences. These applications include embedded data apps that evolve static dashboards into highly customized, dynamic features, internal data products tailored for specific business units, and consumer-facing apps requiring high performance and design customization. Built on cloud data warehouses, data applications utilize a headless BI layer for data modeling, access control, and caching, and are developed by integrating modern engineering workflows. While the foundational layers of the data stack remain consistent across application types, no code/low code tools and frameworks like Appsmith, Retool, Plotly Dash, and Streamlit facilitate the development of analytics interfaces and shareable web applications. As the technology stack advances, data applications will expand in number and variety, offering new opportunities for handling larger and more complex datasets, although traditional dashboard-centric experiences will continue to fulfill specific needs.