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The Australian Square Kilometre Array Pathfinder (ASKAP) collects images ofthe sky at radio wavelengths with an unprecedented field of view, combined witha high angular resolution and sub-millijansky sensitivities. The large quantityof data produced is used by the ASKAP Variables and Slow Transients (VAST)survey science project to study the dynamic radio sky. Efficient pipelines arevital in such research, where searches often form a `needle in a haystack' typeof problem to solve. However, the existing pipelines developed among theradio-transient community are not suitable for the scale of ASKAP datasets.  In this paper we provide a technical overview of the new "VAST Pipeline": amodern and scalable Python-based data pipeline for transient searches, usingup-to-date dependencies and methods. The pipeline allows source association tobe performed at scale using the Pandas DataFrame interface and the well-knownAstropy crossmatch functions. The Dask Python framework is used to paralleliseoperations as well as scale them both vertically and horizontally, by means ofa cluster of workers. A modern web interface for data exploration and queryinghas also been developed using the latest Django web framework combined withBootstrap.

A scalable transient detection pipeline for the Australian SKA Pathfinder VAST survey