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The advancement of technology in telescopes and observation instruments has allowed more and more data to be available with higher resolutions and quality, but the way to obtain this data is the same, astronomers are conditioned obtain observation time. Once the data is public in most cases fall into disuse and except with initiatives such as virtual observatories that seek to offer this data over standards and protocols for storage and access, the data are not used again. Our proposal seeks to recover these large volumes of observations already made and offer them as a cloud service from a High Performance Computing environment to be reprocessed by the scientific community with the aim of generating new science from them, for this in the Chilean Virtual observatory we have created ChiVOLabs, a system that has a microservice architecture on Docker that allows us, based on searches in our virtual observatories, to offer the possibility of reprocessing ALMA data through a Jupyter Notebook interface. The process start using the IVOA protocol: Simple Cone Search to find the astronomical data and then select the option to reprocess them. Our architecture create a DOCKER environment with the entire analysis stack of ChiVO and Python in our datacenter, furthermore transforms the reduction script used to generate the original product in a jupyter notebook automatically connected to our CASA cluster (Pipleine of reduction of ALMA). Finally we search in our datalake raw ALMA data, known as ADSM, from which is generated the selected product and are created a link to these raw in the container. With this we offer the scientific community the possibility of reprocessing these data according to their scientific interests and generating new data products that can finally be downloaded or continue to be working with our libraries using the full power of our datacenter
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