Recent technological advancements have increased the amount of data produced day by day, resulting in an increasing need for real-time data analyses. To fulfill these needs, companies have started to shift their traditional computing systems with other systems in which data integration has a pivotal role for a successful implementation. Therefore, due to its complexity, to guarantee a successful implementation of a data pipeline, an initial effort should be initially placed on the process modelling for the identification of all actors involved. Based on that context, the present paper aims to investigate the main contributions of the state-of-the-art related to the conceptual modelling of data pipelines. For that, a systematic literature review (SLR) was conducted to identify the existing alternatives and extensions of current notations (e.g.: BPMN, UML, etc.) for the conceptual modelling of data pipelines. As result, not only several notations could be identified, but also some gaps related to the modelling of elements emerged with Big Data.