Integrating heterogeneous datasets from several sources is a common bioinformatics task that often requires implementing a complex workflow intermixing database access, data filtering, format conversions, identifier mapping, among further diverse operations. Data integration is especially important when annotating next generation sequencing data, where a multitude of diverse tools and heterogeneous databases can be used to provide a large variety of annotation for genomic locations, such a single nucleotide variants or genes. Each tool and data source is potentially useful for a given project and often more than one are used in parallel for the same purpose. However, software that always produces all available data is difficult to maintain and quickly leads to an excess of data, creating an information overload rather than the desired goal-oriented and integrated result.