{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":39,"n_cols":5,"rows_kept":39,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[3,2,3,3],[4,2,4,3],[5,2,5,3],[15,2,15,3],[16,2,16,3],[19,2,19,3],[19,4,19,5],[20,2,20,3],[20,4,20,5],[21,2,21,3],[21,4,21,5],[22,2,22,3],[23,2,23,3],[24,2,24,3],[25,2,25,3],[26,2,26,3],[27,2,27,3],[32,2,32,3],[33,2,33,3],[36,2,36,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996376450303316"]
[null,null,"Titolo",null,"Aethiopica : international journal of Ethiopian and Eritrean studies"]
[null,null,"Pubbl/distr/stampa",null,"Hamburg : , : Hamburg University Press, , 1998-"]
[null,null,"ISSN",null,"2194-4024"]
[null,null,"Descrizione fisica",null,"Annual"]
[null,null,"Soggetti",null,"Philology - Ethiopia"]
[{"$nulls":4},"Linguistics - Ethiopia"]
[{"$nulls":4},"Archaeology - Ethiopia"]
[{"$nulls":4},"Ethnology - Ethiopia"]
[{"$nulls":4},"Archaeology"]
[{"$nulls":4},"Ethnology"]
[{"$nulls":4},"Linguistics"]
[{"$nulls":4},"Philology"]
[{"$nulls":4},"Periodicals."]
[{"$nulls":4},"Ethiopia"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Periodico"]
[null,null,null,"Note generali","Refereed/Peer-reviewed"]
[null,"2.","Record Nr.",null,"UNINA9910427051203321"]
[null,null,"Autore",null,"Chawla Harsh"]
[null,null,"Titolo",null,"Data Lake Analytics on Microsoft Azure : A Practitioner's Guide to Big Data Engineering / / by Harsh Chawla, Pankaj Khattar"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2020"]
[null,null,"ISBN",null,"1-4842-6252-2"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XVII, 222 p. 134 illus.)"]
[null,null,"Disciplina",null,"004.165"]
[null,null,"Soggetti",null,"Microsoft software"]
[{"$nulls":4},"Microsoft .NET Framework"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Microsoft"]
[{"$nulls":4},"Big Data"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Chapter 1: Data Lake Analytics Concepts -- Chapter 2: Building Blocks of Data Analytics -- Chapter 3: Data Analytics on Public Cloud -- Chapter 4: Data Ingestion -- Chapter 5: Data Storage -- Chapter 6: Data Preparation and Training Part I -- Chapter 7: Data Preparation and Training Part II -- Chapter 8: Model and Serve -- Chapter 9: Summary."]
[null,null,null,"Sommario/riassunto","Get a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will learn from the authors’ experience working with large-scale enterprise customer engagements. This book includes comprehensive coverage of how: To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions Data platform professionals who have been working on relational data stores, non-relational data stores, and bigdata technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and "]
[{"$nulls":4},"various artificial intelligence and machine learning technologies available on Microsoft Azure. You will understand the: Concepts of data lake analytics, the modern data warehouse, and advanced data analytics Architecture patterns of the modern data warehouse and advanced data analytics solutions Phases—such as Data Ingestion, Store, Prep and Train, and Model and Serve—of data analytics solutions and technology choices available on Azure under each phase In-depth coverage of real-time and batch mode data analytics solutions architecture Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight."]