03836ntm a22005537i 4500 000259874 CZ-PrVSE 20220604071223.0 m d cr n|||||||||| 220604s2022 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Holub, Ondřej ISIS:118860 dis Analýza data lakehouse : Nejnovější evoluce v big data architekturách a její přínosy pro data science eng Analyzing Data Lakehouse : The Latest Evolution of Big Data Architectures and Its Benefits for Data Science Ondřej Holub 2022 ?? stran : digital, PDF soubor Vedoucí práce: Petr Máša Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2022 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2022 The data lakehouse is a new-generation distributed data analytics platform architecture that combines key benefits of data lakes and warehouses into a unified design, reconciling diverse data science and business intelligence workloads over a common data foundation. This thesis examines the novel data lakehouse architecture and a set of associated data preprocessing and data science technologies and demonstrates how data science projects can benefit from them. First, the author describes and analyzes the emerging lakehouse architectural pattern with a primary focus on data storage, integration, and preprocessing as an integral lifecycle stage for data and knowledge mining projects and methods such as machine learning. Second, the thesis describes modules of the Apache Spark ecosystem relevant for machine learning and streaming. Third, the author formulates a subset of challenges observed in current enterprise big data preprocessing and machine learning practice and verifies how integrating described technologies can lead to partially or fully answering such challenges and ultimately increase the business value gained from data science. This work presents a proof of concept implementation illustrating streaming data preprocessing, interactive machine learning feature engineering and modeling, and streaming predictions in the context of a simulated data science project, demonstrating the resolution of the defined data engineering challenges observed in the data science lifecycle, such as supporting data reproducibility and handling upserts, using distributed data lakehouse technologies in a cloud setting. Způsob přístupu: Internet znalostní a webové technologie [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas data lakehouse data science data engineering big data machine learning Delta Lake Apache Spark Máša, Petr ISIS:17194 ths Rauch, Jan, 1948- jn20001227021 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/75024/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/75024 Hlavní práce https://insis.vse.cz/zp/75024/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/75024/posudek/oponent/74663 Oponentura https://insis.vse.cz/zp/75024/priloha/23614 Přiloha k práci https://insis.vse.cz/zp/75024/podrobnosti dc:identifier NEPOSILAT VSKP vse75024 220602 75024