LEADER 00877nam0-22003011i-450- 001 990007524030403321 005 20060420124527.0 035 $a000752403 035 $aFED01000752403 035 $a(Aleph)000752403FED01 035 $a000752403 100 $a20030814d1878----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ab-------001yy 200 1 $aFerrovia Roma - Sulmona (per Tivoli - Avezzano - Molina)$econsiderazioni$fdi Alfonso Audinot 210 $aRoma$cArtero$d1878. 215 $a56 p., 1 c. ripieg.$d24 cm 610 0 $aItalia$aFerrovie 610 0 $aABRUZZO 700 1$aAudinot,$bAlfonso$0271781 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007524030403321 952 $aE-08-032$bIst.8320$fILFGE 959 $aILFGE 996 $aFerrovia Roma - Sulmona (per Tivoli - Avezzano - Molina$9684044 997 $aUNINA LEADER 01646nam0 22003131i 450 001 UON00026021 005 20231205102040.139 100 $a20020107f |0itac50 ba 101 $ager$aPER 102 $aIR 105 $a|||| 1|||| 200 1 $aPersien vor 113 jahren$etext und bilder, 1. teil: Esfahan$fErnst Holtzer$gzusammengestelt und ubersetzt von Mohammad Assemi 210 $a[Tehran]$cZentrum fur die persische ethnologie$d[n.d.] 112$d352 p.$cill. ; 29 cm Altro front.: Iran dar yeksad o sizdah sal-e pis 510 1$3UON00354480$aIran dar yeksad o sizdah sal-e pis 606 $aIRAN$xREPERTORI FOTOGRAFICI$3UONC007305$2FI 606 $aARCHITETTURA$xIRAN$xISFAHAN$3UONC008648$2FI 620 $aIR$dTihra?n$3UONL005570 686 $aIRA XI$cIRAN - ARCHITETTURA E URBANISTICA$2A 700 1$aHOLTZER$bErnst$3UONV017713$0643275 702 1$aASEMI$bMohammad$3UONV017380 712 $aMarkaz-e mardomsenasi$3UONV248033$4650 790 1$aASSEMI, Mohammad$zASEMI, Mohammad$3UONV017381 801 $aIT$bSOL$c20240220$gRICA 912 $aUON00026021 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI IRA XI 006 BIS $eSI SA 84362 7 006 BIS 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI IRA XI 006 $eSI SA 3177 7 006 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI MUSEO SCERRATO 255 $eSI 17816 7 255 996 $aPersien vor 113 jahren$91201179 997 $aUNIOR LEADER 00963nam0 22002771i 450 001 UON00516711 005 20231205105524.937 010 $a38-286-0024-7 100 $a20230808d1998 |0itac50 ba 101 $ager 102 $aDE 105 $a|||| ||||| 200 1 $aErnst Jünger$eeine Biographie$fPaul Noack 210 $aBerlin$cAlexander Fest$d1998 215 $a368 p.,$cill.$d22 cm 606 $aBIOGRAFIA$3UONC042374$2FI 620 $aDE$dBerlin$3UONL003157 676 $a838.91$cMiscellanea tedesca. 1900-1990$v21 700 1$aNOACK$bPaul$3UONV291659$0245412 712 $aFest$3UONV269124$4650 801 $aIT$bSOL$c20240220$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00516711 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI F. Goethe 830 JUE 4 3116 $eSI 44947 5 3116 996 $aErnst Jünger$93903834 997 $aUNIOR LEADER 04700nam 22006015 450 001 9910584482303321 005 20230804132038.0 010 $a9781484282335 010 $a1484282337 024 7 $a10.1007/978-1-4842-8233-5 035 $a(MiAaPQ)EBC7044684 035 $a(Au-PeEL)EBL7044684 035 $a(CKB)24243655900041 035 $a(OCoLC)1336459705 035 $a(OCoLC-P)1336459705 035 $a(DE-He213)978-1-4842-8233-5 035 $a(PPN)263902676 035 $a(CaSebORM)9781484282335 035 $a(Perlego)4514171 035 $a(EXLCZ)9924243655900041 100 $a20220713d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Azure Data Lakehouse Toolkit $eBuilding and Scaling Data Lakehouses on Azure with Delta Lake, Apache Spark, Databricks, Synapse Analytics, and Snowflake /$fby Ron L'Esteve 205 $a1st ed. 2022. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2022. 215 $a1 online resource (467 pages) 300 $aIncludes index. 311 08$a9781484282328 311 08$a1484282329 320 $aIncludes index. 327 $aPart I: Getting Started -- Chapter 1: The Data Lakehouse Paradigm -- Part II: Data Platforms -- Chapter 2: Snowflake -- Chapter 3: Databricks -- Chapter 4: Synapse Analytics -- Part III: Apache Spark ELT -- Chapter 5: Pipelines and Jobs -- Chapter 6: Notebook Code -- Part IV: Delta Lake.-Chapter 7: Schema Evolution -- Chapter 8: Change Feed -- Chapter 9: Clones -- Chapter 10: Live Tables -- Chapter 11: Sharing -- Part V: Optimizing Performance -- Chapter 12: Dynamic Partition Pruning for Querying Star Schemas -- Chapter 13: Z-Ordering & Data Skipping -- Chapter 14: Adaptive Query Execution -- Chapter 15: Bloom Filter Index -- Chapter 16: Hyperspace -- Part VI: Advanced Capabilities -- Chapter 17: Auto Loader -- Chapter 18: Python Wheels -- Chapter 19: Security & Controls. 330 $aDesign and implement a modern data lakehouse on the Azure Data Platform using Delta Lake, Apache Spark, Azure Databricks, Azure Synapse Analytics, and Snowflake. This book teaches you the intricate details of the Data Lakehouse Paradigm and how to efficiently design a cloud-based data lakehouse using highly performant and cutting-edge Apache Spark capabilities using Azure Databricks, Azure Synapse Analytics, and Snowflake. You will learn to write efficient PySpark code for batch and streaming ELT jobs on Azure. And you will follow along with practical, scenario-based examples showing how to apply the capabilities of Delta Lake and Apache Spark to optimize performance, and secure, share, and manage a high volume, high velocity, and high variety of data in your lakehouse with ease. The patterns of success that you acquire from reading this book will help you hone your skills to build high-performing and scalable ACID-compliant lakehouses using flexible and cost-efficient decoupled storage and compute capabilities. Extensive coverage of Delta Lake ensures that you are aware of and can benefit from all that this new, open source storage layer can offer. In addition to the deep examples on Databricks in the book, there is coverage of alternative platforms such as Synapse Analytics and Snowflake so that you can make the right platform choice for your needs. After reading this book, you will be able to implement Delta Lake capabilities, including Schema Evolution, Change Feed, Live Tables, Sharing, and Clones to enable better business intelligence and advanced analytics on your data within the Azure Data Platform. What You Will Learn Implement the Data Lakehouse Paradigm on Microsoft?s Azure cloud platform Benefit from the new Delta Lake open-source storage layer for data lakehouses Take advantage of schema evolution, change feeds, live tables, and more Write functional PySpark code for data lakehouse ELT jobs Optimize Apache Spark performance through partitioning, indexing, and other tuning options Choose between alternatives such as Databricks, Synapse Analytics, and Snowflake. 606 $aMicrosoft Azure (Computing platform) 606 $aCloud computing 606 $aElectronic data processing 606 $aDatabases 615 0$aMicrosoft Azure (Computing platform) 615 0$aCloud computing. 615 0$aElectronic data processing. 615 0$aDatabases. 676 $a004.6782 700 $aL'Esteve$b Ron$01251647 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910584482303321 996 $aThe Azure Data Lakehouse Toolkit$92901394 997 $aUNINA