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One of the major challenges providing large databases like the WFCAM ScienceArchive (WSA) is to minimize ingest times for pixel/image metadata andcatalogue data. In this article we describe how the pipeline processed data areingested into the database as the first stage in building a release databasewhich will be succeeded by advanced processing (source merging, seaming,detection quality flagging etc.). To accomplish the ingestion procedure as fastas possible we use a mixed Python/C++ environment and run the required tasks ina simple parallel modus operandi where the data are split into daily chunks andthen processed on different computers. The created data files can be ingestedinto the database immediately as they are available. This flexible way ofhandling the data allows the most usage of the available CPUs as the comparisonwith sequential processing shows.

A pseudo-parallel Python environment for database curation