Verfügbarkeit: SAP data intelligence

SAP data intelligence: the comprehensive Guide

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Hauptverfasser:	Teja Atluri, Dharma ca. 20./21. Jh (VerfasserIn), Bardhan, Devraj ca. 20./21. Jh (VerfasserIn), Ghosh, Santanu (VerfasserIn), Ghosh, Snehasish ca. 20./21 Jh (VerfasserIn), Saha, Arindom ca. 20./21. Jh (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	New York, NY Rheinwerk Publishing 2022 SAP PRESS 2022
Ausgabe:	1. Auflage
Schriftenreihe:	SAP PRESS Englisch
Schlagworte:	SAP AG Cloud Computing Business Intelligence Maschinelles Lernen SAP DI SAP Data Hub SAP Cloud Platform (SAP CP) SAP Business Technology Platform (SAP BTP)
Online-Zugang:	Inhaltstext Inhaltsverzeichnis
Beschreibung:	783 Seiten Illustrationen, Diagramme 25.4 cm x 17.8 cm
ISBN:	9781493221622 1493221620

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Datensatz im Suchindex

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adam_text	Contents at a Glance PART I Getting Started 1 The Data Fabric for the Intelligent Enterprise...................................... 33 2 Architecture and Capabilities............................................................... 51 3 Setup and Installation.......................................................................... 93 4 Using SAP Data Intelligence Applications............................................ 169 PART II Data Management, Orchestration, and Machine Learning 5 Metadata-Driven Data Governance...................................................... 193 6 Modeling Data Processing Pipelines..................................................... 237 7 Creating Operators and Data Types...................................................... 275 8 Building Docker Images........................................................................ 295 9 Machine Learning................................................................................. 309 10 Jupyter Notebook.................................................................................. 373 11 SAP Data Intelligence Python SDK........................................................ 439 PART III Integration 12 Integrating with ABAP Systems............................................................ 459 13 Integrating with Non-SAP Systems....................................................... 497 14 Integrating Big Data Workloads with SAP Vora.................................... 515 15 Integrating with SAP Data Warehouse Cloud....................................... 543 16 Integrating with SAP Analytics Cloud................................................... 571 PART IV System Management, Security, and Operations 17 Administration...................................................................................... 595 18 Security.................................................................................................. 639 19 Maintenance......................................................................................... 661 20 Application Lifecycle Management....................................................... 673 21 Business Content and Use Cases.......................................................... 737 Contents Preface.............................................................................................................................. 21 Parti Getting Started 1 The Data Fabric for the Intelligent Enterprise зз 1.1 Data Fabric..................................................................................................... 34 1.1.1 Trends..................................................................................................... 35 1.1.2 Benefits.................................................................................................. 37 1.2 Data Orchestration........................................................................................ 38 1.3 SAP Business Technology Platform.............................................................. 40 1.4 SAP Data Intelligence................................................................................... 43 1.5 Summary....................................................................................................... 50 2 Architecture andCapabilities 51 2.1 Genesis of SAP Data Intelligence.................................................................. 52 2.1.1 Features from SAP Leonardo Machine Learning Foundation............... 54 2.1.2 Evolution from SAP Data Hub to SAP Data Intelligence........................ 58 2.2 SAP Data Intelligence Architecture.............................................................. 60 2.3 Deployment Options and Bring Your Own License Model........................... 63 2.4 Kübemetes Cluster and Containers.............................................................. 68 2.4.1 Overview of Kübemetes......................................................................... 68 2.4.2 Kübemetes Cluster Architecture........................................................... 75 2.4.3 Container Runtimes............................................................................... 78 2.4.4 Pods and Workloads............................................................................... 79 2.4.5 Resources and Policies........................................................................... 81 2.4.6 Kübemetes and SAP Data Intelligence.................................................. 83 SAP Data Intelligence Launchpad................................................................ 86 2.5.1 Persona-Based Application.................................................................... 86 2.5.2 Overview of Applications....................................................................... 88 Summary....................................................................................................... 91 2.5 2.6 7 Contents 3 3.1 3.2 3.3 3.4 3.5 Setup and Installation 93 Landscape Sizing............................................................................................ 93 3.1.1 Sizing Various SAP Data Intelligence Components ............................... 94 3.1.2 Minimum Sizing and Initial Sizing for SAP Data Intelligence................ 95 3.1.3 Understanding the T-Shirt Sizing Approach .......................................... 99 SAP Cloud Appliance Library.......................................................................... 99 3.2.1 Getting Started with SAP Cloud Appliance Library................................ 101 3.2.2 Deploying SAP Solutions in the Cloud.................................................... 103 3.2.3 Activating and Creating Solution Instances.......................................... 105 3.2.4 Security Considerations for SAP Cloud Appliance Library..................... 106 On-Demand Cloud Provisioning and Instance Sizing.................................. 107 3.3.1 Sizing with SAP Cloud Appliance Library................................................ 108 3.3.2 Supported Cloud Providers for SAP Cloud Appliance Library................ 109 3.3.3 Understanding Costs and Payments...................................................... 109 3.3.4 Backing Up, Restoring, and Terminating an Instance............................ 112 Setting Up SAP Data Intelligence on SAP Cloud Appliance Library............. 113 3.4.1 Prerequisites for Cloud Provider Account.............................................. 114 3.4.2 Connedingto SAP Cloud Appliance Library.......................................... 122 3.4.3 Creating and Accessing the Solution..................................................... 124 3.4.4 Accessing the Jump Box for Monitoring and Troubleshooting............ 136 3.4.5 Running the Solution.............................................................................. 145 3.4.6 Access through Browser Using Local Hosts File.................................... 148 3.4.7 Personalization........................................................................................ 149 SAP Data Intelligence 3.0 Installation On-Premise..................................... 150 3.5.1 Planning and Prerequisites for an On-Premise Installation.................. 150 3.5.2 Modular Deployment with SLC Bridge................................................... 151 3.5.3 Installing SAP Data Intelligence with the Maintenance Planner and SLC Bridge......................................................................... 154 3.6 Summary....................................................................................................... 4 Using SAP Data Intelligence Applications i69 4.1 SAP Data Intelligence Launchpad Applications........................................... 169 4.2 Applications for Data Engineers................................................................... 172 4.2.1 Connection Management..................................................................... 172 4.2.2 Metadata Explorer................................................................................. 174 4.2.3 Modeler.................................................................................................. 175 8 168 Contents Customer Data Export.......................................................................... 176 Applications for Data Scientists................................................................... 177 4.2.4 4.3 4.4 4.5 4.6 4.3.1 ML Scenario Manager............................................................................. 177 4.3.2 Vora Tools ............................................................................................... 178 Applications for Modelers and Auditors..................................................... 179 4.4.1 Monitoring Applications......................................................................... 180 4.4.2 Audit and System Logs........................................................................... 181 Applications for System Administrators..................................................... 182 4.5.1 Policy Management................................................................................ 182 4.5.2 Handling Privileges................................................................................. 184 4.5.3 System Management............................................................................. 184 4.5.4 License Management.............................................................................. 188 Summary...................................................................................................... 189 Partii Data Management, Orchestration, and Machine Learning 5 Metadata-Driven Data Governance 193 5.1 Metadata Explorer for Data Governance..................................................... 194 5.1.1 Intelligent Information Management with the Discovery Dashboard............................................................................ 5.2 5.3 5.4 195 5.1.2 Metadata Crawlers to Explore, Classify, and Label Data Assets........... 196 5.1.3 Managing Metadata Data across a Connected System Landscape..... 196 Data Profiling to Understand Data.............................................................. 197 5.2.1 Profiling Data Sets from Connections................................................... 198 5.2.2 Profiling Adions and Monitor................................................................ 198 5.2.3 Viewing Profile Fact Sheets.................................................................... 199 Managing Publications and Data Catalogs.................................................. 202 5.3.1 Catalogof Published Data Sets ............................................................. 202 5.3.2 Automatic Tags and Hierarchical Tagging............................................. 207 5.3.3 Using Tags as Search Filters.................................................................... 211 5.3.4 Managing Publications in the Catalog................................................... 211 5.3.5 Lineage Depth Set in Publication Processing......................................... 214 Defining Data Quality Rules and Running Rulebooks................................ 214 5.4.1 Rules Determining Business Data Compliance..................................... 215 5.4.2 Categories to Organize Business Rules.................................................. 219 9 Contents 5.4.3 Using the Match Pattern Operator........................................................ 5.4.4 Running and Monitoring Rulebooks...................................................... 221 5.4.5 Business Glossary of Terms and Definitions........................................ 228 Data Lineage from Transformation History................................................ 230 5.5.1 Lineage Analyses for Tracing Data Sets to Sources............................... 230 5.5.2 Lineage Extraction and Supported Sources........................................... 231 5.5.3 Understanding and Configuring the Lineage View.............................. 234 5.6 Summary........................................................................................................ 235 6 Modeling Data Processing Pipelines 5.5 6.1 6.2 6.3 6.4 6.5 10 220 237 Using the SAP Data Intelligence Modeler.................................................... 237 6.1.1 Flow-Based Paradigm as a Network of Information............................. 238 6.1.2 Data Pipeline Engine in the Flow-Based Modeler................................. 239 6.1.3 Navigating the Modeler Panes and Toolbars........................................ 240 6.1.4 Built-In Operators.................................................................................... 242 6.1.5 Creating and Validating Graphs............................................................. 244 Creating and Managing Connections........................................................... 250 6.2.1 Creating Connections............................................................................. 250 6.2.2 Connecting to Cloud Foundry................................................................. 251 6.2.3 Managing Certificates............................................................................ 253 6.2.4 Authorizations for Connertions............................................................. 254 Self-Service Data Preparation with the Metadata Explorer........................ 255 6.3.1 Preparing Data for Accurate Results and Better Insights...................... 255 6.3.2 Self-Service Data Preparation with the Metadata Explorer................... 255 6.3.3 Transforming Structured Data Sets...................................................... 256 6.3.4 Managing Data Preparation Actions...................................................... 258 6.3.5 Processing Data Preparation Actions..................................................... 259 Integrating, Processing, and Orchestrating Workflows.............................. 261 6.4.1 Graph Snippets as a Group of Operators............................................... 262 6.4.2 Working with Data Workflow Operators............................................... 264 6.4.3 Integrating SAP Cloud Applications....................................................... 266 6.4.4 Change Data Capture Graph.................................................................. 267 6.4.5 Custom Operators................................................................................... 267 Scheduling and Monitoring Data Pipelines................................................. 270 6.5.1 Scheduling and Monitoring Data Pipelines............................................ 270 6.5.2 Trace Messages....................................................................................... 272 Contents 6.5.3 Tracking Model Metrics......................................................................... 273 6.5.4 Kübemetes Dashboard and Cluster Logs............................................. 273 6.6 Summary....................................................................................................... 273 7 Creating Operators and Data Types 275 7.1 Creating Custom Operators.......................................................................... 276 7.1.1 Visibility of Events................................................................................. 277 7.1.2 Compatibility of Port Types.................................................................... 277 7.1.3 Creating and Editing Operators............................................................. 281 Implementing Runtime Operators............................................................... 288 7.2.1 Subengines in SAP Data Intelligence Modeler....................................... 288 7.2.2 Working with Subengines to Create Operators..................................... 289 Creating Data Types...................................................................................... 290 7.3.1 Predefined Global Sea lar Types............................................................. 291 7.3.2 Defining Your Own Custom Data Types............................................... 292 7.3.3 Leveraging Data Types in Graphs.......................................................... 293 7.4 Summary....................................................................................................... 293 8 Building Docker images 295 8.1 Containers in Pods and Pods in Clusters..................................................... 295 8.1.1 Delivery of Data-Driven Applications.................................................... 295 8.1.2 Helm: Package Manager for Kübemetes............................................... 296 8.1.3 Dockerfiles: Predefined Runtime Environments.................................. 297 Assembling a Docker Image......................................................................... 298 8.2.1 Building Docker Images through Dockerfiles....................................... 298 8.2.2 Enhancing Docker Images with Different Package Managers............. 302 8.3 Dockerfile Inheritance................................................................................... 303 8.4 Using Docker with Python............................................................................ 305 8.5 Summary....................................................................................................... 308 7.2 7.3 8.2 11 Contents 9 9.1 9.2 9.3 9.4 9.5 Machine Learning Machine Learning with SAP.......................................................................... зо9 310 9.1.1 Machine Learning Solutions in the SAP Landscape............................... 311 9.1.2 TEI Methodology in Machine Learning.................................................. 313 9.1.3 Transforming Business Use Cases with Machine Learning.................. 318 9.1.4 Data-Driven Approach versus Traditional Rule-Based Approach.......... 319 9.1.5 Machine Learning Tasks in Enterprise Contexts.................................... 321 9.1.6 Architedural Principles for Machine Learning...................................... 325 Machine Learning with SAP Data Intelligence............................................. 328 9.2.1 Scalable Data Pipelines in Complex Data Landscapes.......................... 329 9.2.2 Data and Algorithms as Assets for Machine Learning........................... 331 9.2.3 Leveraging Open-Source Environments and Skills................................ 331 Using the ML Scenario Manager................................................................... 333 9.3.1 ML Scenario Manager Overview............................................................. 333 9.3.2 Setting Up a Scenario in ML Scenario Manager.................................... 334 9.3.3 Integrating Hyperscale Data and Targets.............................................. 339 9.3.4 Leveraging Scenario Templates for Machine Learning.......................... 340 9.3.5 Dockerfile Building and Grouping......................................................... 345 9.3.6 Implementing TensorFlow Pipelines..................................................... 347 9.3.7 Training and Deploying Models with New Versions............................. 350 9.3.8 Metrics Explorerand Machine Learning Tracking SDK.......................... 360 9.3.9 Run Collection and Run Performance..................................................... 363 9.3.10 Visualizing SAP Data Intelligence Metrics with SAP Analytics Cloud ... 363 ML Data Manager In Data Workspaces and Data Collerions..................... 365 9.4.1 Data Workspacesand Data Collections................................................ 365 9.4.2 Organizing Data Sets in Data Lakes....................................................... 367 9.4.3 Curating a Data Collection..................................................................... 368 9.4.4 Registeringa Data Set............................................................................ 369 Summary....................................................................................................... 371 10 Jupyter Notebook 373 10.1 Jupyter Notebook Fundamentals................................................................. 374 10.1.1 Interarive Tool for Data Science Projeds............................................. 374 10.1.2 Jupyter Notebook Dashboard and User Interface................................ 379 10.1.3 Data Analysis in Jupyter Notebook....................................................... 381 Ί2 Contents 10.2 Working with SAP HANA Cloud....................................................................... 386 10.2.1 SAP HANA Cloud: Cloud Database as a Service.................................... 387 10.2.2 Exploring SAP HANA Cloud on an SAP BTP Trial Account.................... Understanding the SAP HANA Cockpit and SAP HANA 389 10.2.3 Database Explorer................................................................... 10.2.4 391 Using Jupyter Notebook in SAP BTP and Integration with SAP HANA Cloud............................................................................. 393 SAP Data Intelligence Connection....................................................... 402 Data Science Experiments with Jupyter Notebook....................................... 405 10.3.1 SAP HANA Embedded Machine Learning............................................. 406 10.3.2 Machine Learning Core Operators....................................................... 413 10.3.3 SAP HANA MLTraining Operator......................................................... 423 10.3.4 SAP HANA ML Inference Operator....................................................... 425 10.4 JupyterLab as the Next-Gen Jupyter Notebook............................................. 430 10.4.1 JupyterLab: The Next-Gen User Interface with Built-In Libraries....... 431 10.4.2 Accessing Jupyter Notebook Artifacts from JupyterLab...................... 434 10.4.3 SAP HANA Python Client API ................................................................ 436 Summary............................................................................................................. 437 10.2.5 10.3 10.5 11 SAP Data Intelligence Python SDK 11.1 11.2 11.3 11.4 439 Using SAP Data Intelligence Python SDK....................................................... 440 11.1.1 Setting a Context in Jupyter Notebook................................................. 440 11.1.2 Data Lake API for SDL............................................................................. 441 11.1.3 Retrieving Machine Learning Scenario Metadata................................. 443 11.1.4 Training Container Usingthe SDK......................................................... 444 11.1.5 Executing and Deploying Pipelines....................................................... 447 Accessing Artifacts Using Methods................................................................ 448 Machine Learning Tracking SDK..................................................................... 450 11.3.1 Initializing Run for an Experiment......................................................... 451 11.3.2 Grouping Runs in Run Collections......................................................... 451 11.3.3 Analyzing Metrics and Logs.................................................................. 454 Summary............................................................................................................ 454 13 Contents Partili Integration 12 Integrating with АВАР Systems 12.1 12.2 459 Integration Scenarios.................................................................................... 459 12.1.1 Scenarios and Use Cases for Integration.............................................. 460 12.1.2 ABAP Metadata in the Metadata Explorer............................................ 461 Provisioning Data from ABAP Systems........................................................ 465 12.2.1 Exposing the CDS View......................................................................... 465 12.2.2 Connection Prerequisites for Data Extraction...................................... 466 12.2.3 Connecting On-Premise Systems with the Cloud Connector.............. 467 Using Operators to Trigger Execution in an ABAP System.......................... 472 12.3.1 ABAP Operators to Trigger Fundion Modules or BAPIs........................ 472 12.3.2 Prerequisites for ABAP Operators in Remote Systems......................... 474 12.4 SAP BW/4HANA and SAP Data Intelligence Hybrid Data Virtualization.... 478 12.3 12.5 12.4.1 Prerequisites in SAP Business Warehouse............................................ 478 12.4.2 Using Connection Type HANA_DB....................................................... 480 12.4.3 Authorization Check for Services.......................................................... 481 12.4.4 SAP BW Operator for Pipeline............................................................... 484 Additional Connectivity............................................................................... 485 12.5.1 SAP Information Steward...................................................................... 485 12.5.2 SAP HANA for SQL Data Warehousing.................................................. 489 12.6 Summary...................................................................................................... 495 13 Integrating with Non-SAP Systems 497 13.1 Non-SAP Cloud System Connectivity........................................................... 14 497 13.1.1 Amazon S3.............................................................................................. 498 13.1.2 Amazon Redshift.................................................................................... 500 13.1.3 Windows Azure Storage Blob................................................................ 501 13.1.4 Microsoft Azure SQL Data Warehouse.................................................. 502 13.1.5 Microsoft Azure Data Lake..................................................................... 503 13.1.6 Google Cloud Storage............................................................................ 506 13.1.7 Google BigQuery..................................................................................... 508 13.1.8 IBM Cloud Storage ................................................................................. 509 Contents Non-SAP On-Premise System Connectivity................................................ 510 13.2.1 Oracle Relational Database Management System.............................. 510 13.2.2 Microsoft SQLServer............................................................................. 512 13.3 Summary..................................................................................................... 513 14 Integrating Big Data Workloads with SAP Vora sis 13.2 14.1 14.2 14.3 SAP Vora in Kübemetes Framework............................................................ 516 14.1.1 System Management............................................................................ 516 14.1.2 SAP Vora Engine Architerture............................................................... 517 14.1.3 Accessing SAP Vora User Interface....................................................... 520 14.1.4 SAP Vora Data Preview.......................................................................... 521 14.1.5 Using SQL Editor..................................................................................... 522 14.1.6 Using SQL Scripts.................................................................................... 523 Data Modeling in SAP Vora.......................................................................... 524 14.2.1 Creating Database Schemas.................................................................. 524 14.2.2 Creating Partition Schemes................................................................... 525 14.2.3 Creating Tables and Views.................................................................... 527 14.2.4 Creating Calculated Columns............................................................... 532 14.2.5 Additional Funrtions for Views............................................................. 533 Hierarchies in SAP Vora ............................................................................... 536 14.3.1 SAP Vora SQL for Hierarchical Data Analysis........................................ 537 14.3.2 Using Adjacency Table to Render a Hierarchy...................................... 539 14.3.3 Caching Hierarchies with Materialized Views...................................... 539 Full-Text Search in SAP Vora........................................................................ 540 14.4.1 Text Analysis Graphs in Modeler.......................................................... 540 14.4.2 Linguistic and Semantic Analysis.......................................................... 541 14.4.3 Full-Text Search on a Document Colledion......................................... 542 14.5 Summary...................................................................................................... 542 15 Integrating with SAP Data Warehouse Cloud 543 15.1 Overview of SAP Data Warehouse Cloud.................................................... 543 14.4 15.2 15.1.1 SAP Cloud Services Ecosystem.............................................................. 544 15.1.2 Setting Up the Trial Tenant................................................................... 546 Understanding Spaces.................................................................................. 549 Spaces as Virtual Workspaces............................................................... 549 15.2.1 15 Contents 15.3 15.2.2 Development in a Space....................................................................... 554 15.2.3 Managing Spaces................................................................................. 556 Exploring Connections and Using the Data Builder.................................... 561 15.3.1 Available Connection Types................................................................. 561 15.3.2 Data Builder: Model to Business Catalog............................................ 562 15.3.3 Space-Aware Integrated Story Builder................................................. 566 15.4 Data Builder in SAP Data Warehouse Cloud versus Pipelines in SAP Data Intelligence........................................................................ 570 15.5 Summary..................................................................................................... 570 16 Integrating with SAP Analytics Cloud 571 16.1 Overview of SAP Analytics Cloud................................................................. 571 16.1.1 Solution to Analyze, Plan, Predict, and Collaborate.............................. 572 16.1.2 Fundamental Components: Data, Models, and Stories........................ 574 Use Operators: Read File, Formatter, and Producer.................................... 582 16.2.1 Read File Operator.................................................................................. 583 16.2.2 Decode Table Operator........................................................................... 584 16.2.3 SAP Analytics Cloud Formatter.............................................................. 585 16.2.4 SAP Analytics Cloud Producer................................................................ 586 16.2 16.3 16.4 Pipelines to Train, Predict, and Visualize Data........................................... 587 16.3.1 Using the Dataset API............................................................................ 587 16.3.2 Data Set Provision and Consumption ................................................... 589 Summary..................................................................................................... 591 Part IV System Management, Security, and Operations 17 Administration 595 17.1 System Management Command-Line Client Reference............................. 595 17.1.1 Command-Line Client for SAP Data Intelligence................................. 596 17.1.2 Usingthe VCTLTool: JavaScript Utility................................................ 597 17.1.3 Useful Commands for Command-Line Client...................................... 598 17.2 Administration Applications........................................................................ 599 17.2.1 Administrator Access........................................................................... 600 17.2.2 System Management........................................................................... 600 16 Contents 17.2.3 License Management............................................................................. 611 17.2.4 Connection Management..................................................................... 613 17.3 Monitoring the SAP Data Intelligence Modeler........................................... 616 17.3.1 Monitoring the Status of Graph Execution........................................... 616 17.3.2 Tracing Messages to Isolate Problemsand Errors................................. 621 17.3.3 Downloading Diagnostic Information for Graphs................................ 623 17.4 SAP Data Intelligence System Logging........................................................ 626 17.4.1 Kübemetes Cluster-Level Logging Mechanism.................................... 627 17.4.2 Browsing Application Logs in the Diagnostics Kibana Web User Interface........................................................................................ 629 17.4.3 Aggregating Logs in External Logging Service...................................... 630 System Diagnostics..................................................................................... 631 17.5.1 SAP Data Intelligence Diagnostics: Diagnostics Grafana.................... 631 17.5.2 Kübemetes Cluster Metrics .................................................................. 633 17.5.3 Integrating Diagnostics with External АРМ Solution.......................... 635 17.6 Summary..................................................................................................... 637 18 Security 639 18.1 Approach to Data Protection....................................................................... 639 17.5 18.2 18.3 18.4 18.1.1 Business Semantics for Industry-Specific Legislations......................... 640 18.1.2 Functions for Data Privacy Compliance................................................. 641 18.1.3 Security Features for Data Protection and Privacy................................ 641 Authenticating Services and Users............................................................. 642 18.2.1 Roles and Scope-Driven User Access Control........................................ 642 18.2.2 SAP BTP User Account and Authentication........................................... 644 18.2.3 Self-Signed Certificate Authority and TLS............................................. 649 18.2.4 Leveraging Policy Management for Access Control.............................. 649 18.2.5 Enabling Security Features on Kübemetes Cluster............................... 657 Securely Connecting On-Premise Systems.................................................. 658 18.3.1 Cloud Connector.................................................................................... 658 18.3.2 Site-to-Site Virtual Private Network..................................................... 659 18.3.3 Virtual Private Cloud Peering................................................................ 659 Summary..................................................................................................... 659 17 Contents 19 Maintenance ббі ■ммявтр^эдяіімііітпііміівжмимиажмямиимммм^^ 19.1 Understanding Operational Modes or Run Levels........................................ 661 19.2 Switching the Platform to Maintenance Mode............................................ 662 19.2.1 Enabling or Disabling Maintenance Mode............................................ 663 19.2.2 Restarting SAP Data Intelligence Services............................................. 664 19.2.3 Setting Up a Remote Connection to SAP............................................... 664 19.3 Increasing System Management Persistent Volume Size........................... 665 19.3.1 Persistent Volume Error Handling........................................................ 19.3.2 Changing the Persistent Storage Size of the SAP VoraDisk Engine...... 667 19.3.3 Changingthe Buffer and File Size of the SAP VoraDisk Engine............ 668 19.4 Performing Backups......................................................................................... 668 19.5 Summary........................................................................................................... 671 20 Application Lifecycle Management 673 20.1 Version Control System.................................................................................... 673 20.2 Git........................................................................................................................ 674 Git Basics and Terminology................................................................... 675 20.2.2 Git Integration and CI/CD Process......................................................... 678 20.2.3 SettingUp Your Environment for Git Workflows................................. 697 20.2.1 20.3 20.4 20.5 20.6 20.7 18 665 Continuous Integration and Continuous Delivery....................................... 707 20.3.1 Continuous Integration Best Practices.................................................. 707 20.3.2 Leveraging SAP Solutions for CI/CD...................................................... 712 DevOps Fundamentals and Tools..................................................................... 713 715 20.4.1 The Core Tenets of DevOps................................................................... 20.4.2 Implement Tooling for DevOps............................................................. 718 20.4.3 DevOps for Hybrid Architectures.......................................................... 719 SAP Data Intelligence as the MLOps Platform................................................ 723 20.5.1 Production Lifecycle of Machine Learning Models................................ 724 20.5.2 MLOps Challenges.................................................................................. 726 20.5.3 MLOps Capabilities................................................................................. 727 Migrating from SAP Leonardo Machine Learning Foundation.................... 730 20.6.1 Bring Your Own Model ........................................................................... 731 20.6.2 Migrating the Training Data.................................................................. 733 20.6.3 Adding the Training Data to a Data Lake.............................................. 734 Summary............................................................................................................. 734 Contents 21 Business Content and Use Cases 737 21.1 Digital Transformation and SAP Data Intelligence...................................... 737 21.2 Business Content by Industry......................................................................... 740 21.3 Finance Use Cases............................................................................................. 746 21.4 Supply Chain Use Cases................................................................................... 747 21.5 Manufacturing Use Cases................................................................................ 749 21.6 Summary............................................................................................................ 751 Appendices 753 A Outlook and Roadmap..................................................................................... 753 B The Authors....................................................................................................... 763 Index......................................................................................................................................... 765 19
adam_txt	Contents at a Glance PART I Getting Started 1 The Data Fabric for the Intelligent Enterprise. 33 2 Architecture and Capabilities. 51 3 Setup and Installation. 93 4 Using SAP Data Intelligence Applications. 169 PART II Data Management, Orchestration, and Machine Learning 5 Metadata-Driven Data Governance. 193 6 Modeling Data Processing Pipelines. 237 7 Creating Operators and Data Types. 275 8 Building Docker Images. 295 9 Machine Learning. 309 10 Jupyter Notebook. 373 11 SAP Data Intelligence Python SDK. 439 PART III Integration 12 Integrating with ABAP Systems. 459 13 Integrating with Non-SAP Systems. 497 14 Integrating Big Data Workloads with SAP Vora. 515 15 Integrating with SAP Data Warehouse Cloud. 543 16 Integrating with SAP Analytics Cloud. 571 PART IV System Management, Security, and Operations 17 Administration. 595 18 Security. 639 19 Maintenance. 661 20 Application Lifecycle Management. 673 21 Business Content and Use Cases. 737 Contents Preface. 21 Parti Getting Started 1 The Data Fabric for the Intelligent Enterprise зз 1.1 Data Fabric. 34 1.1.1 Trends. 35 1.1.2 Benefits. 37 1.2 Data Orchestration. 38 1.3 SAP Business Technology Platform. 40 1.4 SAP Data Intelligence. 43 1.5 Summary. 50 2 Architecture andCapabilities 51 2.1 Genesis of SAP Data Intelligence. 52 2.1.1 Features from SAP Leonardo Machine Learning Foundation. 54 2.1.2 Evolution from SAP Data Hub to SAP Data Intelligence. 58 2.2 SAP Data Intelligence Architecture. 60 2.3 Deployment Options and Bring Your Own License Model. 63 2.4 Kübemetes Cluster and Containers. 68 2.4.1 Overview of Kübemetes. 68 2.4.2 Kübemetes Cluster Architecture. 75 2.4.3 Container Runtimes. 78 2.4.4 Pods and Workloads. 79 2.4.5 Resources and Policies. 81 2.4.6 Kübemetes and SAP Data Intelligence. 83 SAP Data Intelligence Launchpad. 86 2.5.1 Persona-Based Application. 86 2.5.2 Overview of Applications. 88 Summary. 91 2.5 2.6 7 Contents 3 3.1 3.2 3.3 3.4 3.5 Setup and Installation 93 Landscape Sizing. 93 3.1.1 Sizing Various SAP Data Intelligence Components . 94 3.1.2 Minimum Sizing and Initial Sizing for SAP Data Intelligence. 95 3.1.3 Understanding the T-Shirt Sizing Approach . 99 SAP Cloud Appliance Library. 99 3.2.1 Getting Started with SAP Cloud Appliance Library. 101 3.2.2 Deploying SAP Solutions in the Cloud. 103 3.2.3 Activating and Creating Solution Instances. 105 3.2.4 Security Considerations for SAP Cloud Appliance Library. 106 On-Demand Cloud Provisioning and Instance Sizing. 107 3.3.1 Sizing with SAP Cloud Appliance Library. 108 3.3.2 Supported Cloud Providers for SAP Cloud Appliance Library. 109 3.3.3 Understanding Costs and Payments. 109 3.3.4 Backing Up, Restoring, and Terminating an Instance. 112 Setting Up SAP Data Intelligence on SAP Cloud Appliance Library. 113 3.4.1 Prerequisites for Cloud Provider Account. 114 3.4.2 Connedingto SAP Cloud Appliance Library. 122 3.4.3 Creating and Accessing the Solution. 124 3.4.4 Accessing the Jump Box for Monitoring and Troubleshooting. 136 3.4.5 Running the Solution. 145 3.4.6 Access through Browser Using Local Hosts File. 148 3.4.7 Personalization. 149 SAP Data Intelligence 3.0 Installation On-Premise. 150 3.5.1 Planning and Prerequisites for an On-Premise Installation. 150 3.5.2 Modular Deployment with SLC Bridge. 151 3.5.3 Installing SAP Data Intelligence with the Maintenance Planner and SLC Bridge. 154 3.6 Summary. 4 Using SAP Data Intelligence Applications i69 4.1 SAP Data Intelligence Launchpad Applications. 169 4.2 Applications for Data Engineers. 172 4.2.1 Connection Management. 172 4.2.2 Metadata Explorer. 174 4.2.3 Modeler. 175 8 168 Contents Customer Data Export. 176 Applications for Data Scientists. 177 4.2.4 4.3 4.4 4.5 4.6 4.3.1 ML Scenario Manager. 177 4.3.2 Vora Tools . 178 Applications for Modelers and Auditors. 179 4.4.1 Monitoring Applications. 180 4.4.2 Audit and System Logs. 181 Applications for System Administrators. 182 4.5.1 Policy Management. 182 4.5.2 Handling Privileges. 184 4.5.3 System Management. 184 4.5.4 License Management. 188 Summary. 189 Partii Data Management, Orchestration, and Machine Learning 5 Metadata-Driven Data Governance 193 5.1 Metadata Explorer for Data Governance. 194 5.1.1 Intelligent Information Management with the Discovery Dashboard. 5.2 5.3 5.4 195 5.1.2 Metadata Crawlers to Explore, Classify, and Label Data Assets. 196 5.1.3 Managing Metadata Data across a Connected System Landscape. 196 Data Profiling to Understand Data. 197 5.2.1 Profiling Data Sets from Connections. 198 5.2.2 Profiling Adions and Monitor. 198 5.2.3 Viewing Profile Fact Sheets. 199 Managing Publications and Data Catalogs. 202 5.3.1 Catalogof Published Data Sets . 202 5.3.2 Automatic Tags and Hierarchical Tagging. 207 5.3.3 Using Tags as Search Filters. 211 5.3.4 Managing Publications in the Catalog. 211 5.3.5 Lineage Depth Set in Publication Processing. 214 Defining Data Quality Rules and Running Rulebooks. 214 5.4.1 Rules Determining Business Data Compliance. 215 5.4.2 Categories to Organize Business Rules. 219 9 Contents 5.4.3 Using the Match Pattern Operator. 5.4.4 Running and Monitoring Rulebooks. 221 5.4.5 Business Glossary of Terms and Definitions. 228 Data Lineage from Transformation History. 230 5.5.1 Lineage Analyses for Tracing Data Sets to Sources. 230 5.5.2 Lineage Extraction and Supported Sources. 231 5.5.3 Understanding and Configuring the Lineage View. 234 5.6 Summary. 235 6 Modeling Data Processing Pipelines 5.5 6.1 6.2 6.3 6.4 6.5 10 220 237 Using the SAP Data Intelligence Modeler. 237 6.1.1 Flow-Based Paradigm as a Network of Information. 238 6.1.2 Data Pipeline Engine in the Flow-Based Modeler. 239 6.1.3 Navigating the Modeler Panes and Toolbars. 240 6.1.4 Built-In Operators. 242 6.1.5 Creating and Validating Graphs. 244 Creating and Managing Connections. 250 6.2.1 Creating Connections. 250 6.2.2 Connecting to Cloud Foundry. 251 6.2.3 Managing Certificates. 253 6.2.4 Authorizations for Connertions. 254 Self-Service Data Preparation with the Metadata Explorer. 255 6.3.1 Preparing Data for Accurate Results and Better Insights. 255 6.3.2 Self-Service Data Preparation with the Metadata Explorer. 255 6.3.3 Transforming Structured Data Sets. 256 6.3.4 Managing Data Preparation Actions. 258 6.3.5 Processing Data Preparation Actions. 259 Integrating, Processing, and Orchestrating Workflows. 261 6.4.1 Graph Snippets as a Group of Operators. 262 6.4.2 Working with Data Workflow Operators. 264 6.4.3 Integrating SAP Cloud Applications. 266 6.4.4 Change Data Capture Graph. 267 6.4.5 Custom Operators. 267 Scheduling and Monitoring Data Pipelines. 270 6.5.1 Scheduling and Monitoring Data Pipelines. 270 6.5.2 Trace Messages. 272 Contents 6.5.3 Tracking Model Metrics. 273 6.5.4 Kübemetes Dashboard and Cluster Logs. 273 6.6 Summary. 273 7 Creating Operators and Data Types 275 7.1 Creating Custom Operators. 276 7.1.1 Visibility of Events. 277 7.1.2 Compatibility of Port Types. 277 7.1.3 Creating and Editing Operators. 281 Implementing Runtime Operators. 288 7.2.1 Subengines in SAP Data Intelligence Modeler. 288 7.2.2 Working with Subengines to Create Operators. 289 Creating Data Types. 290 7.3.1 Predefined Global Sea lar Types. 291 7.3.2 Defining Your Own Custom Data Types. 292 7.3.3 Leveraging Data Types in Graphs. 293 7.4 Summary. 293 8 Building Docker images 295 8.1 Containers in Pods and Pods in Clusters. 295 8.1.1 Delivery of Data-Driven Applications. 295 8.1.2 Helm: Package Manager for Kübemetes. 296 8.1.3 Dockerfiles: Predefined Runtime Environments. 297 Assembling a Docker Image. 298 8.2.1 Building Docker Images through Dockerfiles. 298 8.2.2 Enhancing Docker Images with Different Package Managers. 302 8.3 Dockerfile Inheritance. 303 8.4 Using Docker with Python. 305 8.5 Summary. 308 7.2 7.3 8.2 11 Contents 9 9.1 9.2 9.3 9.4 9.5 Machine Learning Machine Learning with SAP. зо9 310 9.1.1 Machine Learning Solutions in the SAP Landscape. 311 9.1.2 TEI Methodology in Machine Learning. 313 9.1.3 Transforming Business Use Cases with Machine Learning. 318 9.1.4 Data-Driven Approach versus Traditional Rule-Based Approach. 319 9.1.5 Machine Learning Tasks in Enterprise Contexts. 321 9.1.6 Architedural Principles for Machine Learning. 325 Machine Learning with SAP Data Intelligence. 328 9.2.1 Scalable Data Pipelines in Complex Data Landscapes. 329 9.2.2 Data and Algorithms as Assets for Machine Learning. 331 9.2.3 Leveraging Open-Source Environments and Skills. 331 Using the ML Scenario Manager. 333 9.3.1 ML Scenario Manager Overview. 333 9.3.2 Setting Up a Scenario in ML Scenario Manager. 334 9.3.3 Integrating Hyperscale Data and Targets. 339 9.3.4 Leveraging Scenario Templates for Machine Learning. 340 9.3.5 Dockerfile Building and Grouping. 345 9.3.6 Implementing TensorFlow Pipelines. 347 9.3.7 Training and Deploying Models with New Versions. 350 9.3.8 Metrics Explorerand Machine Learning Tracking SDK. 360 9.3.9 Run Collection and Run Performance. 363 9.3.10 Visualizing SAP Data Intelligence Metrics with SAP Analytics Cloud . 363 ML Data Manager In Data Workspaces and Data Collerions. 365 9.4.1 Data Workspacesand Data Collections. 365 9.4.2 Organizing Data Sets in Data Lakes. 367 9.4.3 Curating a Data Collection. 368 9.4.4 Registeringa Data Set. 369 Summary. 371 10 Jupyter Notebook 373 10.1 Jupyter Notebook Fundamentals. 374 10.1.1 Interarive Tool for Data Science Projeds. 374 10.1.2 Jupyter Notebook Dashboard and User Interface. 379 10.1.3 Data Analysis in Jupyter Notebook. 381 Ί2 Contents 10.2 Working with SAP HANA Cloud. 386 10.2.1 SAP HANA Cloud: Cloud Database as a Service. 387 10.2.2 Exploring SAP HANA Cloud on an SAP BTP Trial Account. Understanding the SAP HANA Cockpit and SAP HANA 389 10.2.3 Database Explorer. 10.2.4 391 Using Jupyter Notebook in SAP BTP and Integration with SAP HANA Cloud. 393 SAP Data Intelligence Connection. 402 Data Science Experiments with Jupyter Notebook. 405 10.3.1 SAP HANA Embedded Machine Learning. 406 10.3.2 Machine Learning Core Operators. 413 10.3.3 SAP HANA MLTraining Operator. 423 10.3.4 SAP HANA ML Inference Operator. 425 10.4 JupyterLab as the Next-Gen Jupyter Notebook. 430 10.4.1 JupyterLab: The Next-Gen User Interface with Built-In Libraries. 431 10.4.2 Accessing Jupyter Notebook Artifacts from JupyterLab. 434 10.4.3 SAP HANA Python Client API . 436 Summary. 437 10.2.5 10.3 10.5 11 SAP Data Intelligence Python SDK 11.1 11.2 11.3 11.4 439 Using SAP Data Intelligence Python SDK. 440 11.1.1 Setting a Context in Jupyter Notebook. 440 11.1.2 Data Lake API for SDL. 441 11.1.3 Retrieving Machine Learning Scenario Metadata. 443 11.1.4 Training Container Usingthe SDK. 444 11.1.5 Executing and Deploying Pipelines. 447 Accessing Artifacts Using Methods. 448 Machine Learning Tracking SDK. 450 11.3.1 Initializing Run for an Experiment. 451 11.3.2 Grouping Runs in Run Collections. 451 11.3.3 Analyzing Metrics and Logs. 454 Summary. 454 13 Contents Partili Integration 12 Integrating with АВАР Systems 12.1 12.2 459 Integration Scenarios. 459 12.1.1 Scenarios and Use Cases for Integration. 460 12.1.2 ABAP Metadata in the Metadata Explorer. 461 Provisioning Data from ABAP Systems. 465 12.2.1 Exposing the CDS View. 465 12.2.2 Connection Prerequisites for Data Extraction. 466 12.2.3 Connecting On-Premise Systems with the Cloud Connector. 467 Using Operators to Trigger Execution in an ABAP System. 472 12.3.1 ABAP Operators to Trigger Fundion Modules or BAPIs. 472 12.3.2 Prerequisites for ABAP Operators in Remote Systems. 474 12.4 SAP BW/4HANA and SAP Data Intelligence Hybrid Data Virtualization. 478 12.3 12.5 12.4.1 Prerequisites in SAP Business Warehouse. 478 12.4.2 Using Connection Type HANA_DB. 480 12.4.3 Authorization Check for Services. 481 12.4.4 SAP BW Operator for Pipeline. 484 Additional Connectivity. 485 12.5.1 SAP Information Steward. 485 12.5.2 SAP HANA for SQL Data Warehousing. 489 12.6 Summary. 495 13 Integrating with Non-SAP Systems 497 13.1 Non-SAP Cloud System Connectivity. 14 497 13.1.1 Amazon S3. 498 13.1.2 Amazon Redshift. 500 13.1.3 Windows Azure Storage Blob. 501 13.1.4 Microsoft Azure SQL Data Warehouse. 502 13.1.5 Microsoft Azure Data Lake. 503 13.1.6 Google Cloud Storage. 506 13.1.7 Google BigQuery. 508 13.1.8 IBM Cloud Storage . 509 Contents Non-SAP On-Premise System Connectivity. 510 13.2.1 Oracle Relational Database Management System. 510 13.2.2 Microsoft SQLServer. 512 13.3 Summary. 513 14 Integrating Big Data Workloads with SAP Vora sis 13.2 14.1 14.2 14.3 SAP Vora in Kübemetes Framework. 516 14.1.1 System Management. 516 14.1.2 SAP Vora Engine Architerture. 517 14.1.3 Accessing SAP Vora User Interface. 520 14.1.4 SAP Vora Data Preview. 521 14.1.5 Using SQL Editor. 522 14.1.6 Using SQL Scripts. 523 Data Modeling in SAP Vora. 524 14.2.1 Creating Database Schemas. 524 14.2.2 Creating Partition Schemes. 525 14.2.3 Creating Tables and Views. 527 14.2.4 Creating Calculated Columns. 532 14.2.5 Additional Funrtions for Views. 533 Hierarchies in SAP Vora . 536 14.3.1 SAP Vora SQL for Hierarchical Data Analysis. 537 14.3.2 Using Adjacency Table to Render a Hierarchy. 539 14.3.3 Caching Hierarchies with Materialized Views. 539 Full-Text Search in SAP Vora. 540 14.4.1 Text Analysis Graphs in Modeler. 540 14.4.2 Linguistic and Semantic Analysis. 541 14.4.3 Full-Text Search on a Document Colledion. 542 14.5 Summary. 542 15 Integrating with SAP Data Warehouse Cloud 543 15.1 Overview of SAP Data Warehouse Cloud. 543 14.4 15.2 15.1.1 SAP Cloud Services Ecosystem. 544 15.1.2 Setting Up the Trial Tenant. 546 Understanding Spaces. 549 Spaces as Virtual Workspaces. 549 15.2.1 15 Contents 15.3 15.2.2 Development in a Space. 554 15.2.3 Managing Spaces. 556 Exploring Connections and Using the Data Builder. 561 15.3.1 Available Connection Types. 561 15.3.2 Data Builder: Model to Business Catalog. 562 15.3.3 Space-Aware Integrated Story Builder. 566 15.4 Data Builder in SAP Data Warehouse Cloud versus Pipelines in SAP Data Intelligence. 570 15.5 Summary. 570 16 Integrating with SAP Analytics Cloud 571 16.1 Overview of SAP Analytics Cloud. 571 16.1.1 Solution to Analyze, Plan, Predict, and Collaborate. 572 16.1.2 Fundamental Components: Data, Models, and Stories. 574 Use Operators: Read File, Formatter, and Producer. 582 16.2.1 Read File Operator. 583 16.2.2 Decode Table Operator. 584 16.2.3 SAP Analytics Cloud Formatter. 585 16.2.4 SAP Analytics Cloud Producer. 586 16.2 16.3 16.4 Pipelines to Train, Predict, and Visualize Data. 587 16.3.1 Using the Dataset API. 587 16.3.2 Data Set Provision and Consumption . 589 Summary. 591 Part IV System Management, Security, and Operations 17 Administration 595 17.1 System Management Command-Line Client Reference. 595 17.1.1 Command-Line Client for SAP Data Intelligence. 596 17.1.2 Usingthe VCTLTool: JavaScript Utility. 597 17.1.3 Useful Commands for Command-Line Client. 598 17.2 Administration Applications. 599 17.2.1 Administrator Access. 600 17.2.2 System Management. 600 16 Contents 17.2.3 License Management. 611 17.2.4 Connection Management. 613 17.3 Monitoring the SAP Data Intelligence Modeler. 616 17.3.1 Monitoring the Status of Graph Execution. 616 17.3.2 Tracing Messages to Isolate Problemsand Errors. 621 17.3.3 Downloading Diagnostic Information for Graphs. 623 17.4 SAP Data Intelligence System Logging. 626 17.4.1 Kübemetes Cluster-Level Logging Mechanism. 627 17.4.2 Browsing Application Logs in the Diagnostics Kibana Web User Interface. 629 17.4.3 Aggregating Logs in External Logging Service. 630 System Diagnostics. 631 17.5.1 SAP Data Intelligence Diagnostics: Diagnostics Grafana. 631 17.5.2 Kübemetes Cluster Metrics . 633 17.5.3 Integrating Diagnostics with External АРМ Solution. 635 17.6 Summary. 637 18 Security 639 18.1 Approach to Data Protection. 639 17.5 18.2 18.3 18.4 18.1.1 Business Semantics for Industry-Specific Legislations. 640 18.1.2 Functions for Data Privacy Compliance. 641 18.1.3 Security Features for Data Protection and Privacy. 641 Authenticating Services and Users. 642 18.2.1 Roles and Scope-Driven User Access Control. 642 18.2.2 SAP BTP User Account and Authentication. 644 18.2.3 Self-Signed Certificate Authority and TLS. 649 18.2.4 Leveraging Policy Management for Access Control. 649 18.2.5 Enabling Security Features on Kübemetes Cluster. 657 Securely Connecting On-Premise Systems. 658 18.3.1 Cloud Connector. 658 18.3.2 Site-to-Site Virtual Private Network. 659 18.3.3 Virtual Private Cloud Peering. 659 Summary. 659 17 Contents 19 Maintenance ббі ■ммявтр^эдяіімііітпііміівжмимиажмямиимммм^^ 19.1 Understanding Operational Modes or Run Levels. 661 19.2 Switching the Platform to Maintenance Mode. 662 19.2.1 Enabling or Disabling Maintenance Mode. 663 19.2.2 Restarting SAP Data Intelligence Services. 664 19.2.3 Setting Up a Remote Connection to SAP. 664 19.3 Increasing System Management Persistent Volume Size. 665 19.3.1 Persistent Volume Error Handling. 19.3.2 Changing the Persistent Storage Size of the SAP VoraDisk Engine. 667 19.3.3 Changingthe Buffer and File Size of the SAP VoraDisk Engine. 668 19.4 Performing Backups. 668 19.5 Summary. 671 20 Application Lifecycle Management 673 20.1 Version Control System. 673 20.2 Git. 674 Git Basics and Terminology. 675 20.2.2 Git Integration and CI/CD Process. 678 20.2.3 SettingUp Your Environment for Git Workflows. 697 20.2.1 20.3 20.4 20.5 20.6 20.7 18 665 Continuous Integration and Continuous Delivery. 707 20.3.1 Continuous Integration Best Practices. 707 20.3.2 Leveraging SAP Solutions for CI/CD. 712 DevOps Fundamentals and Tools. 713 715 20.4.1 The Core Tenets of DevOps. 20.4.2 Implement Tooling for DevOps. 718 20.4.3 DevOps for Hybrid Architectures. 719 SAP Data Intelligence as the MLOps Platform. 723 20.5.1 Production Lifecycle of Machine Learning Models. 724 20.5.2 MLOps Challenges. 726 20.5.3 MLOps Capabilities. 727 Migrating from SAP Leonardo Machine Learning Foundation. 730 20.6.1 Bring Your Own Model . 731 20.6.2 Migrating the Training Data. 733 20.6.3 Adding the Training Data to a Data Lake. 734 Summary. 734 Contents 21 Business Content and Use Cases 737 21.1 Digital Transformation and SAP Data Intelligence. 737 21.2 Business Content by Industry. 740 21.3 Finance Use Cases. 746 21.4 Supply Chain Use Cases. 747 21.5 Manufacturing Use Cases. 749 21.6 Summary. 751 Appendices 753 A Outlook and Roadmap. 753 B The Authors. 763 Index. 765 19
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author	Teja Atluri, Dharma ca. 20./21. Jh Bardhan, Devraj ca. 20./21. Jh Ghosh, Santanu Ghosh, Snehasish ca. 20./21 Jh Saha, Arindom ca. 20./21. Jh
author_GND	(DE-588)126178362X (DE-588)126225910X (DE-588)171883489 (DE-588)1261784200 (DE-588)1261784448
author_facet	Teja Atluri, Dharma ca. 20./21. Jh Bardhan, Devraj ca. 20./21. Jh Ghosh, Santanu Ghosh, Snehasish ca. 20./21 Jh Saha, Arindom ca. 20./21. Jh
author_role	aut aut aut aut aut
author_sort	Teja Atluri, Dharma ca. 20./21. Jh
author_variant	a d t ad adt d b db s g sg s g sg a s as
building	Verbundindex
bvnumber	BV048209669
classification_rvk	ST 530
ctrlnum	(OCoLC)1309398487 (DE-599)DNB1236767969
discipline	Informatik
discipline_str_mv	Informatik
edition	1. Auflage
format	Book
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illustrated	Illustrated
index_date	2024-07-03T19:48:11Z
indexdate	2024-07-10T09:32:05Z
institution	BVB
institution_GND	(DE-588)106510992X
isbn	9781493221622 1493221620
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-033590533
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publishDate	2022
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publisher	Rheinwerk Publishing SAP PRESS
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spelling	Teja Atluri, Dharma ca. 20./21. Jh. Verfasser (DE-588)126178362X aut SAP data intelligence the comprehensive Guide Dharma Teja Atluri, Devraj Bardhan, Santanu Ghosh, Snehasish Ghosh, Arindom Saha 1. Auflage 202201 New York, NY Rheinwerk Publishing 2022 SAP PRESS 2022 783 Seiten Illustrationen, Diagramme 25.4 cm x 17.8 cm txt rdacontent n rdamedia nc rdacarrier SAP PRESS Englisch SAP AG (DE-588)5091643-9 gnd rswk-swf Cloud Computing (DE-588)7623494-0 gnd rswk-swf Business Intelligence (DE-588)4588307-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf SAP DI SAP Data Hub SAP Cloud Platform (SAP CP) SAP Business Technology Platform (SAP BTP) Maschinelles Lernen (DE-588)4193754-5 s SAP AG (DE-588)5091643-9 b Cloud Computing (DE-588)7623494-0 s Business Intelligence (DE-588)4588307-5 s DE-604 Bardhan, Devraj ca. 20./21. Jh. Verfasser (DE-588)126225910X aut Ghosh, Santanu Verfasser (DE-588)171883489 aut Ghosh, Snehasish ca. 20./21 Jh. Verfasser (DE-588)1261784200 aut Saha, Arindom ca. 20./21. Jh. Verfasser (DE-588)1261784448 aut Galileo Press Inc. (DE-588)106510992X pbl X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=5f7855eab1cd4be2bcf869e4f8a5cb9d&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033590533&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20210709 DE-101 https://d-nb.info/provenance/plan#vlb
spellingShingle	Teja Atluri, Dharma ca. 20./21. Jh Bardhan, Devraj ca. 20./21. Jh Ghosh, Santanu Ghosh, Snehasish ca. 20./21 Jh Saha, Arindom ca. 20./21. Jh SAP data intelligence the comprehensive Guide SAP AG (DE-588)5091643-9 gnd Cloud Computing (DE-588)7623494-0 gnd Business Intelligence (DE-588)4588307-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd
subject_GND	(DE-588)5091643-9 (DE-588)7623494-0 (DE-588)4588307-5 (DE-588)4193754-5
title	SAP data intelligence the comprehensive Guide
title_auth	SAP data intelligence the comprehensive Guide
title_exact_search	SAP data intelligence the comprehensive Guide
title_exact_search_txtP	SAP data intelligence the comprehensive Guide
title_full	SAP data intelligence the comprehensive Guide Dharma Teja Atluri, Devraj Bardhan, Santanu Ghosh, Snehasish Ghosh, Arindom Saha
title_fullStr	SAP data intelligence the comprehensive Guide Dharma Teja Atluri, Devraj Bardhan, Santanu Ghosh, Snehasish Ghosh, Arindom Saha
title_full_unstemmed	SAP data intelligence the comprehensive Guide Dharma Teja Atluri, Devraj Bardhan, Santanu Ghosh, Snehasish Ghosh, Arindom Saha
title_short	SAP data intelligence
title_sort	sap data intelligence the comprehensive guide
title_sub	the comprehensive Guide
topic	SAP AG (DE-588)5091643-9 gnd Cloud Computing (DE-588)7623494-0 gnd Business Intelligence (DE-588)4588307-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd
topic_facet	SAP AG Cloud Computing Business Intelligence Maschinelles Lernen
url	http://deposit.dnb.de/cgi-bin/dokserv?id=5f7855eab1cd4be2bcf869e4f8a5cb9d&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033590533&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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