PPT-Apache Kylin 2.0 From classic OLAP to real-time data warehouse

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Apache Kylin 2.0 From classic OLAP to real-time data warehouse: Transcript

Apache Kylin 20 From classic OLAP to realtime data warehouse 李扬 Li Yang Cofounder amp CTO All rights reserved Kyligence Inc httpkyligenceio BI Visualization HDFS Apache Kylin Hive. Tanvi. . Madgavkar. CSE 7330. FALL 2009. What is Data Warehousing?. Ralph Kimball states . that . :. A . data warehouse . is a . copy of transaction . data . specifically structured for query and analysis. 31.03.2014. (. Muscat. , . Oman. ). OUTLINE. Users Problems and necessity for Data Warehouse. BI . Definition. . and. . Components. Data Warehousing Concepts . DW Goals and Objectives . OLAP and OLTP terms . Introduction . Apache Storm is a real-time fault-tolerant and distributed Stream Processing Engine.. Open Sourced September 19. th. 2011. Main languages-. Clojure. and the JAVA.. Some of the Characteristics of storm are Fast, Scalable, Fault-tolerant, Reliable and Easy to operate.. Chapter 4. . Data Warehousing and On-line Analytical Processing. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign, . 2017. 1. Chapter 4: Data Warehousing and On-line Analytical Processing. By -. . Pooja. . Sinha. . Kaushalya. . Bakde. Data Warehouse. . “A data warehouse is a . subject-oriented. , . integrated. , . time-variant. , and . nonvolatile. collection of data in support of management’s decision-making process.”—W. H. Inmon. . on. . Petabytes. . of. . Data. . on. . Azure. Yang. . Li. Co-founder & CTO, . Kyligence. Qixiao Wang. Principal Technical Evangelist, Microsoft. James Baker. Principal Software Engineer, Microsoft. Dr. Nicholas Gibbins – . nmg@ecs.soton.ac.uk. 2012-2013. Processing Styles – OLTP. 2. On-Line Transaction Processing. Traditional workloads. ,. . ‘. bread . and . butter. ’. processing . Volumes of data. Corey . Pentasuglia. Masters Project. 5/11/2016. Examiners. Dr. Scott . Spetka. Dr. . Bruno . Andriamanalimanana. Dr. Roger . Cavallo. Masters Project Objectives. Research DML (Distributed Machine Learning). 2. Data, Data everywhere. yet .... I can’t find the data I need. data is scattered over the network. many versions, subtle differences. I can’t get the data I need. need an expert to get the data. and Projects. ABDS in Summary XIX: Layer 14B. . Data . Science Curriculum. March 1 2015. Geoffrey Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Enterprise Data World 2016. SanDiego. , Ca – April 17-22, 2016. Luminita . Vollmer. luminita.vollmer@gmail.com. agenda. Intro. It’s 2016 – what’s new!. Use cases for data. Everyday pressures and big data. Apache NiFi Presented by: Joe Witt Apache NiFi PPMC Member Apache NiFi’s job: Enterprise Dataflow Management 1 Automate the flow of data from any source …to systems which extract meaning and . — Chapter 3 —. Data Warehouse and OLAP Technology: An Overview. 2. Chapter 3: Data Warehousing and OLAP Technology: An Overview. What is a data warehouse? . A multi-dimensional data model. Data warehouse architecture. . subject-oriented. ,. integrated. , . time-variant. , . and . nonvolatile. . collection of data in support of management’s decision-making process.. A decision support database that is maintained .

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