Diploma Thesis focuses on integrating Hadoop platform into current data warehouse architecture. In theoretical part, properties of Big Data are described together with their methods and processing models. Hadoop framework, its components and distributions are discussed. Moreover, compoments which enables end users, developers and analytics to access Hadoop cluster are described. Case study of batch data extraction from current data warehouse on Oracle platform with aid of Sqoop tool, their transformation in relational structures of Hive component and uploading them back to the original source is being discussed at practical part of thesis. Compression of data and efficiency of queries depending on various storage formats is also discussed. Quality and consistency of manipulated data is checked during all phases of the process. Fraction of practical part discusses ways of storing and capturing stream data. For this purposes tool Flume is used to capture stream data. Further this data are transformed in Pig tool. Purpose of implementing the process is to move part of data and its processing from current data warehouse to Hadoop cluster. Therefore process of integration of current data warehouse and Hortonworks Data Platform and its components, was designed

The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored  in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are connected to this system via an information system, which is able to automatically generate notifications when monitored values are out of range.