To provide good results and decisions in data-driven systems, data quality must be ensured as a primary consideration. An important aspect of this is data cleaning. Although many different algorithms and tools already exist for data cleaning, an end-to-end data quality solution is still needed. In this paper, we present FONDUE, our vision of a well-founded end-to-end data quality optimizer. In contrast to many studies that consider data cleaning in the context of machine learning, our approach focuses on various scenarios, such as when preprocessing and downstream analysis are separated. As an adaptive and easily extendable framework, FONDUE operates similarly to proven methods of database query optimization. Analogously, it consists of the following parts: Rule-based optimization, where the appropriate data cleaning algorithms are selected based on use case constraints, optimizer hints in the form of best practices, and cost-based optimization, where the costs are measured in terms of data quality. Accordingly, the result is an optimized data cleaning pipeline. The choice of different optimization goals enables further flexibility, e.g. for environments with limited resources. As a first building block of FONDUE, we present CheDDaR, which is used to detect errors and measure data quality. Both are important tasks for improving data quality with FONDUE.