This work explores empirically the Apache Hadoop in the context of outbound open innovation (OI) in small and medium- sized enterprises (SMEs) through the lens of innovation streams. The Apache Hadoop is a free and open source (F/OSS) library of codes for distributed computer processing, and it is the industry standard for big data analysis. We are living in the big data age and this research focus on big data analysis digital service platforms. Organisations have radically changed the way they store, manipulate, and create value from information. These data were seen, not very long time ago, as worthless. Businesses are obtaining data from different sources and in diverse formats, and advancing new products and services. Organisations need to explore and exploit niche F/OSS products and services based on outbound OI. Some private sector SMEs are short of tools and require more awareness of the potential benefits of outbound OI for product and service development and the lens of innovation streams offers a multitude of opportunities for analysis. New concepts of value production were brought to light by the notion of OI, including F/OSS. Some private sector businesses lack desorptive capacity, and the proposed conceptual model advances an alternative to the status quo. There is a substantial sum of works on F/OSS, OI and service digital platforms. References to these subjects through the lens of innovation streams in the particular context of the outbound OI in SMEs within the Apache Hadoop appear to be very limited, and there are very few examples of similar studies in this area. Outbound OI is still a major challenge for most firms, some authorities have highlighted the lack of research in the field and expressed the need for complementary studies. Innovation streams are a set of innovations that build upon the current products and services of an organisation, extend that organisation’s technical direction, and/or help it diversify into different markets. Outbound OI in F/OSS SMEs’ technology spin-offs relates to the innovation streams paradigm in terms of discontinuous innovation. While Michael Tushman and his colleagues have formulated innovation streams in detail, the relation of this framework to the F/OSS outbound OI debate within the Apache Hadoop in SMEs is taken for granted. Many questions regarding this relationship still remain, and this work addresses some of these unanswered issues. This doctoral research endorses the view of an evident limitation in the outbound OI literature, replies to aforementioned calls for more research, and adds to prior analyses by advancing new tools for the comprehension of the role of outbound OI in SMEs. It adds to the emergent body of empirical work on the Apache Hadoop and the current frame of literature on service digital platforms. Its potential findings have implications for both academia and organisations offering big data products and services. Drawing on the qualitative interpretive case study tradition, this research explores theoretical ideas and relates them to the real-world context of Apache Hadoop. This interpretive case study offers suggestions to the following overall research questions: (1) How do innovation streams within the Apache Hadoop evolve from explorative to exploitative and, finally, branch out into new markets? (2) How can we promote and sustain innovation streams within the Apache Hadoop in SMEs, in the context of outbound OI? (3) Can a conceptual model be built? (4) Are these methods adaptable?