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Analytics Data Pipeline in the Cloud for an SME : A Case Study

Show simple item record 2020-05-20T09:47:21Z 2020-05-20T09:47:21Z 2020-05-20
dc.title Analytics Data Pipeline in the Cloud for an SME : A Case Study en
ethesis.discipline none und
ethesis.department none und
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty.URI Helsingin yliopisto fi University of Helsinki en Helsingfors universitet sv
dct.creator Meriläinen, Roosa
dct.issued 2020
dct.language.ISO639-2 eng
dct.abstract In the world of constantly growing data masses the efficient extraction, saving and accessing that data for business intelligence and analytics has become increasingly important to businesses. Analytics and business intelligence software is offered by many providers in the market for all sizes of organizations and there are multiple ways to build an analytics system, or pipeline from scratch or integrated with tools available on the market. In this case study we explore and re-design the analytics pipeline solution of a medium sized software product company by utilizing the design science research methodology. We discuss the current technologies and tools on the market for business intelligence and analytics and consider how they fit into our case study context. As design science suggests, we design, implement and evaluate two prototypes of an analyt- ics pipeline with an Extract, Transform and Load (ETL) solution and data warehouse. The prototypes represent two different approaches to building an analytics pipeline - an in-house approach, and a partially outsourced approach. Our study brings out typical challenges similar businesses may face when designing and building their own business intelligence and analytics software. In our case we lean towards an analytics pipeline with an outsourced ETL process to be able to pass various different types of event data with a consistent data schema into our data warehouse with minimal maintenance work. However, we also show the value of near real time analytics with an in-house solution, and offer some ideas on how such a pipeline may be built. en
dct.subject business intelligence
dct.subject analytics
dct.subject SME
dct.subject software architecture
dct.subject ETL
dct.subject design science research
dct.language en
ethesis.isPublicationLicenseAccepted false
ethesis.language English en
ethesis.language englanti fi
ethesis.language engelska sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype pro gradu-avhandlingar sv
dct.identifier.ethesis E-thesisID:a1188d23-8be0-4e37-8db5-6497ab488bd3
dct.identifier.urn URN:NBN:fi:hulib-202005202219
dc.type.dcmitype Text
ethesis.facultystudyline Ohjelmistojärjestelmät fi
ethesis.facultystudyline Software systems en
ethesis.facultystudyline Mjukvarusystem sv
ethesis.mastersdegreeprogram Tietojenkäsittelytieteen maisteriohjelma fi
ethesis.mastersdegreeprogram Master's Programme in Computer Science en
ethesis.mastersdegreeprogram Magisterprogrammet i datavetenskap sv

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