Today, advanced data analysts make use of both predictive models and optimization problem solving to build data-driven decision making applications, a combination of technologies recently termed Prescriptive Analytics (PA). Current PA applications typically have multiple layers of poorly integrated components: a relational DBMS for data storage/management, ML tools for prediction, and specialized software packages for problem modeling and optimization problem solving. This complex stack leads to inefficient, labor-intensive, and error-prone PA workflows, blocking wider adoption of PA. In this paper, we present SolveDB+ -an RDBMS for PA applications which supports all PA steps with modeling, predictive, and optimization functionalities, and integrates these in a common SQL-based framework. Major SolveDB+ novelties are 1) a powerful SQL-based approach for PA problem specification and solving, 2) an extensible in-DBMS infrastructure for prediction and optimization solvers, and 3) in-DBMS modeling and management of PA models. SolveDB+ significantly improves both PA developer productivity and performance.