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The main purpose of this thesis is to describe how an integration of a Hadoop platform into currently existing Business Intelligence technologies and processes can augment its data processing and analysis capabilities while encountering Big Data. Furthermore, it describes reasons why the whole Hadoop application ecosystem was founded and informs the reader about the functionality of its primary components. It continues with provision of overview about Hadoop higher-level components architecture and their use in existing Business Intelligence processes such as data ingestion, transformation and analysis. In the last theoretical chapter it focuses itself on describing specific areas of utilization of the Hadoop platform and Big Data in data warehousing, text mining and predictive analytics. From the practical point of view, a particular use case is provided, an implementation of Big Data ETL process in the field of financial markets and trading with a detailed explanation of the corresponding necessities such as data model, ETL code and proposed metrics, which can be further implemented for achieving increased return on investments. |
