00897nam0-22003011i-450-99000170764040332120070529153438.0000170764FED01000170764(Aleph)000170764FED0100017076420030910d1884----km-y0itay50------bafreFR<<L'>>aménagement des forêtstraité pratique de la conduite des exploitations de forêts en taillis et en futaiepar Alfred Puton3e éd.ParisJ. Rothschild[1884]218 p.18 cmAmministrazione forestale634.928Puton,Alfred<1832-1893>73613ITUNINARICAUNIMARCBK99000170764040332160 634.928 C 1336FAGBCFAGBCAménagement des forêts361826UNINA00878nam a2200253 i 450099100323004970753620020509114442.0010704s1987 uk ||| | eng 0851154468b1112894x-39ule_instPARLA177756ExLDip.to Filosofiaita133.509Tester, S. Jim539876A history of western astrology /S.J. TesterWoodbridge, Suffolk :Boydell press,1987VIII, 256 p. :ill. ;24 cm.AstrologiaStoria.b1112894x21-09-0628-06-02991003230049707536LE005IF LIII D 61LE005IFA-3176le005-E0.00-l- 00000.i1126755028-06-02History of western astrology872066UNISALENTOle00501-01-01ma -enguk 2103793nam 2200529 450 991042705120332120210303233922.01-4842-6252-210.1007/978-1-4842-6252-8(CKB)4100000011493404(DE-He213)978-1-4842-6252-8(MiAaPQ)EBC6370241(CaSebORM)9781484262528(PPN)252511662(EXLCZ)99410000001149340420210303d2020 uy 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierData lake analytics on Microsoft Azure a practitioner's guide to big data engineering /Harsh Chawla; Pankaj Khattar; Sandeep J. Alur1st ed. 2020.New York, New York :Apress L. P.,[2020]©20201 online resource (XVII, 222 p. 134 illus.) Includes index.1-4842-6251-4 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.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 big data 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 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.Big dataMicrosoft Azure (Computing platform)Microsoft .NET FrameworkBig data.Microsoft Azure (Computing platform)Microsoft .NET Framework.004.165Chawla Harsh995906Khattar PankajAlur J. SandeepMiAaPQMiAaPQMiAaPQBOOK9910427051203321Data lake analytics on Microsoft Azure2282142UNINA