{"product_id":"official-google-cloud-certified-professional-data-engineer-study-guide-paperback-softback-9781119618430","title":"Official Google Cloud Certified Professional Data Engineer Study Guide (Paperback \/ softback) 9781119618430","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOfficial Google Cloud Certified Professional Data Engineer Study Guide\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eDan Sullivan (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119618430, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 7 June 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e23.4 x 18.5 x 2 cm, 0.499 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe proven Study Guide that prepares you for this new Google Cloud exam\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eGoogle Cloud Certified Professional Data Engineer Study Guide\u003c\/i\u003e, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. \u003c\/p\u003e \u003cp\u003eWritten by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, \u003ci\u003eGoogle Cloud Certified Professional Data Engineer Study Guide \u003c\/i\u003eis your ace in the hole for deploying and managing analytics and machine learning applications. \u003c\/p\u003e \u003cul\u003e \u003cli\u003eBuild and operationalize storage systems, pipelines, and compute infrastructure\u003c\/li\u003e \u003cli\u003eUnderstand machine learning models and learn how to select pre-built models\u003c\/li\u003e \u003cli\u003eMonitor and troubleshoot machine learning models\u003c\/li\u003e \u003cli\u003eDesign analytics and machine learning applications that are secure, scalable, and highly available. \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIntroduction xxiii\u003c\/p\u003e \u003cp\u003eAssessment Test xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Selecting Appropriate Storage Technologies 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFrom Business Requirements to Storage Systems 2\u003c\/p\u003e \u003cp\u003eIngest 3\u003c\/p\u003e \u003cp\u003eStore 5\u003c\/p\u003e \u003cp\u003eProcess and Analyze 6\u003c\/p\u003e \u003cp\u003eExplore and Visualize 8\u003c\/p\u003e \u003cp\u003eTechnical Aspects of Data: Volume, Velocity, Variation, Access, and Security 8\u003c\/p\u003e \u003cp\u003eVolume 8\u003c\/p\u003e \u003cp\u003eVelocity 9\u003c\/p\u003e \u003cp\u003eVariation in Structure 10\u003c\/p\u003e \u003cp\u003eData Access Patterns 11\u003c\/p\u003e \u003cp\u003eSecurity Requirements 12\u003c\/p\u003e \u003cp\u003eTypes of Structure: Structured, Semi-Structured, and Unstructured 12\u003c\/p\u003e \u003cp\u003eStructured: Transactional vs. Analytical 13\u003c\/p\u003e \u003cp\u003eSemi-Structured: Fully Indexed vs. Row Key Access 13\u003c\/p\u003e \u003cp\u003eUnstructured Data 15\u003c\/p\u003e \u003cp\u003eGoogle’s Storage Decision Tree 16\u003c\/p\u003e \u003cp\u003eSchema Design Considerations 16\u003c\/p\u003e \u003cp\u003eRelational Database Design 17\u003c\/p\u003e \u003cp\u003eNoSQL Database Design 20\u003c\/p\u003e \u003cp\u003eExam Essentials 23\u003c\/p\u003e \u003cp\u003eReview Questions 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Building and Operationalizing Storage Systems 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCloud SQL 30\u003c\/p\u003e \u003cp\u003eConfiguring Cloud SQL 31\u003c\/p\u003e \u003cp\u003eImproving Read Performance with Read Replicas 33\u003c\/p\u003e \u003cp\u003eImporting and Exporting Data 33\u003c\/p\u003e \u003cp\u003eCloud Spanner 34\u003c\/p\u003e \u003cp\u003eConfiguring Cloud Spanner 34\u003c\/p\u003e \u003cp\u003eReplication in Cloud Spanner 35\u003c\/p\u003e \u003cp\u003eDatabase Design Considerations 36\u003c\/p\u003e \u003cp\u003eImporting and Exporting Data 36\u003c\/p\u003e \u003cp\u003eCloud Bigtable 37\u003c\/p\u003e \u003cp\u003eConfiguring Bigtable 37\u003c\/p\u003e \u003cp\u003eDatabase Design Considerations 38\u003c\/p\u003e \u003cp\u003eImporting and Exporting 39\u003c\/p\u003e \u003cp\u003eCloud Firestore 39\u003c\/p\u003e \u003cp\u003eCloud Firestore Data Model 40\u003c\/p\u003e \u003cp\u003eIndexing and Querying 41\u003c\/p\u003e \u003cp\u003eImporting and Exporting 42\u003c\/p\u003e \u003cp\u003eBigQuery 42\u003c\/p\u003e \u003cp\u003eBigQuery Datasets 43\u003c\/p\u003e \u003cp\u003eLoading and Exporting Data 44\u003c\/p\u003e \u003cp\u003eClustering, Partitioning, and Sharding Tables 45\u003c\/p\u003e \u003cp\u003eStreaming Inserts 46\u003c\/p\u003e \u003cp\u003eMonitoring and Logging in BigQuery 46\u003c\/p\u003e \u003cp\u003eBigQuery Cost Considerations 47\u003c\/p\u003e \u003cp\u003eTips for Optimizing BigQuery 47\u003c\/p\u003e \u003cp\u003eCloud Memorystore 48\u003c\/p\u003e \u003cp\u003eCloud Storage 50\u003c\/p\u003e \u003cp\u003eOrganizing Objects in a Namespace 50\u003c\/p\u003e \u003cp\u003eStorage Tiers 51\u003c\/p\u003e \u003cp\u003eCloud Storage Use Cases 52\u003c\/p\u003e \u003cp\u003eData Retention and Lifecycle Management 52\u003c\/p\u003e \u003cp\u003eUnmanaged Databases 53\u003c\/p\u003e \u003cp\u003eExam Essentials 54\u003c\/p\u003e \u003cp\u003eReview Questions 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Designing Data Pipelines 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOverview of Data Pipelines 62\u003c\/p\u003e \u003cp\u003eData Pipeline Stages 63\u003c\/p\u003e \u003cp\u003eTypes of Data Pipelines 66\u003c\/p\u003e \u003cp\u003eGCP Pipeline Components 73\u003c\/p\u003e \u003cp\u003eCloud Pub\/Sub 74\u003c\/p\u003e \u003cp\u003eCloud Dataflow 76\u003c\/p\u003e \u003cp\u003eCloud Dataproc 79\u003c\/p\u003e \u003cp\u003eCloud Composer 82\u003c\/p\u003e \u003cp\u003eMigrating Hadoop and Spark to GCP 82\u003c\/p\u003e \u003cp\u003eExam Essentials 83\u003c\/p\u003e \u003cp\u003eReview Questions 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Designing a Data Processing Solution 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDesigning Infrastructure 90\u003c\/p\u003e \u003cp\u003eChoosing Infrastructure 90\u003c\/p\u003e \u003cp\u003eAvailability, Reliability, and Scalability of Infrastructure 93\u003c\/p\u003e \u003cp\u003eHybrid Cloud and Edge Computing 96\u003c\/p\u003e \u003cp\u003eDesigning for Distributed Processing 98\u003c\/p\u003e \u003cp\u003eDistributed Processing: Messaging 98\u003c\/p\u003e \u003cp\u003eDistributed Processing: Services 101\u003c\/p\u003e \u003cp\u003eMigrating a Data Warehouse 102\u003c\/p\u003e \u003cp\u003eAssessing the Current State of a Data Warehouse 102\u003c\/p\u003e \u003cp\u003eDesigning the Future State of a Data Warehouse 103\u003c\/p\u003e \u003cp\u003eMigrating Data, Jobs, and Access Controls 104\u003c\/p\u003e \u003cp\u003eValidating the Data Warehouse 105\u003c\/p\u003e \u003cp\u003eExam Essentials 105\u003c\/p\u003e \u003cp\u003eReview Questions 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Building and Operationalizing Processing Infrastructure 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProvisioning and Adjusting Processing Resources 112\u003c\/p\u003e \u003cp\u003eProvisioning and Adjusting Compute Engine 113\u003c\/p\u003e \u003cp\u003eProvisioning and Adjusting Kubernetes Engine 118\u003c\/p\u003e \u003cp\u003eProvisioning and Adjusting Cloud Bigtable 124\u003c\/p\u003e \u003cp\u003eProvisioning and Adjusting Cloud Dataproc 127\u003c\/p\u003e \u003cp\u003eConfiguring Managed Serverless Processing Services 129\u003c\/p\u003e \u003cp\u003eMonitoring Processing Resources 130\u003c\/p\u003e \u003cp\u003eStackdriver Monitoring 130\u003c\/p\u003e \u003cp\u003eStackdriver Logging 130\u003c\/p\u003e \u003cp\u003eStackdriver Trace 131\u003c\/p\u003e \u003cp\u003eExam Essentials 132\u003c\/p\u003e \u003cp\u003eReview Questions 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Designing for Security and Compliance 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentity and Access Management with Cloud IAM 140\u003c\/p\u003e \u003cp\u003ePredefined Roles 141\u003c\/p\u003e \u003cp\u003eCustom Roles 143\u003c\/p\u003e \u003cp\u003eUsing Roles with Service Accounts 145\u003c\/p\u003e \u003cp\u003eAccess Control with Policies 146\u003c\/p\u003e \u003cp\u003eUsing IAM with Storage and Processing Services 148\u003c\/p\u003e \u003cp\u003eCloud Storage and IAM 148\u003c\/p\u003e \u003cp\u003eCloud Bigtable and IAM 149\u003c\/p\u003e \u003cp\u003eBigQuery and IAM 149\u003c\/p\u003e \u003cp\u003eCloud Dataflow and IAM 150\u003c\/p\u003e \u003cp\u003eData Security 151\u003c\/p\u003e \u003cp\u003eEncryption 151\u003c\/p\u003e \u003cp\u003eKey Management 153\u003c\/p\u003e \u003cp\u003eEnsuring Privacy with the Data Loss Prevention API 154\u003c\/p\u003e \u003cp\u003eDetecting Sensitive Data 154\u003c\/p\u003e \u003cp\u003eRunning Data Loss Prevention Jobs 155\u003c\/p\u003e \u003cp\u003eInspection Best Practices 156\u003c\/p\u003e \u003cp\u003eLegal Compliance 156\u003c\/p\u003e \u003cp\u003eHealth Insurance Portability and Accountability Act (HIPAA) 156\u003c\/p\u003e \u003cp\u003eChildren’s Online Privacy Protection Act 157\u003c\/p\u003e \u003cp\u003eFedRAMP 158\u003c\/p\u003e \u003cp\u003eGeneral Data Protection Regulation 158\u003c\/p\u003e \u003cp\u003eExam Essentials 158\u003c\/p\u003e \u003cp\u003eReview Questions 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Designing Databases for Reliability, Scalability, and Availability 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDesigning Cloud Bigtable Databases for Scalability and Reliability 166\u003c\/p\u003e \u003cp\u003eData Modeling with Cloud Bigtable 166\u003c\/p\u003e \u003cp\u003eDesigning Row-keys 168\u003c\/p\u003e \u003cp\u003eDesigning for Time Series 170\u003c\/p\u003e \u003cp\u003eUse Replication for Availability and Scalability 171\u003c\/p\u003e \u003cp\u003eDesigning Cloud Spanner Databases for Scalability and Reliability 172\u003c\/p\u003e \u003cp\u003eRelational Database Features 173\u003c\/p\u003e \u003cp\u003eInterleaved Tables 174\u003c\/p\u003e \u003cp\u003ePrimary Keys and Hotspots 174\u003c\/p\u003e \u003cp\u003eDatabase Splits 175\u003c\/p\u003e \u003cp\u003eSecondary Indexes 176\u003c\/p\u003e \u003cp\u003eQuery Best Practices 177\u003c\/p\u003e \u003cp\u003eDesigning BigQuery Databases for Data Warehousing 179\u003c\/p\u003e \u003cp\u003eSchema Design for Data Warehousing 179\u003c\/p\u003e \u003cp\u003eClustered and Partitioned Tables 181\u003c\/p\u003e \u003cp\u003eQuerying Data in BigQuery 182\u003c\/p\u003e \u003cp\u003eExternal Data Access 183\u003c\/p\u003e \u003cp\u003eBigQuery ML 185\u003c\/p\u003e \u003cp\u003eExam Essentials 185\u003c\/p\u003e \u003cp\u003eReview Questions 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Understanding Data Operations for Flexibility and Portability 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCataloging and Discovery with Data Catalog 192\u003c\/p\u003e \u003cp\u003eSearching in Data Catalog 193\u003c\/p\u003e \u003cp\u003eTagging in Data Catalog 194\u003c\/p\u003e \u003cp\u003eData Preprocessing with Dataprep 195\u003c\/p\u003e \u003cp\u003eCleansing Data 196\u003c\/p\u003e \u003cp\u003eDiscovering Data 196\u003c\/p\u003e \u003cp\u003eEnriching Data 197\u003c\/p\u003e \u003cp\u003eImporting and Exporting Data 197\u003c\/p\u003e \u003cp\u003eStructuring and Validating Data 198\u003c\/p\u003e \u003cp\u003eVisualizing with Data Studio 198\u003c\/p\u003e \u003cp\u003eConnecting to Data Sources 198\u003c\/p\u003e \u003cp\u003eVisualizing Data 200\u003c\/p\u003e \u003cp\u003eSharing Data 200\u003c\/p\u003e \u003cp\u003eExploring Data with Cloud Datalab 200\u003c\/p\u003e \u003cp\u003eJupyter Notebooks 201\u003c\/p\u003e \u003cp\u003eManaging Cloud Datalab Instances 201\u003c\/p\u003e \u003cp\u003eAdding Libraries to Cloud Datalab Instances 202\u003c\/p\u003e \u003cp\u003eOrchestrating Workflows with Cloud Composer 202\u003c\/p\u003e \u003cp\u003eAirflow Environments 203\u003c\/p\u003e \u003cp\u003eCreating DAGs 203\u003c\/p\u003e \u003cp\u003eAirflow Logs 204\u003c\/p\u003e \u003cp\u003eExam Essentials 204\u003c\/p\u003e \u003cp\u003eReview Questions 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Deploying Machine Learning Pipelines 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStructure of ML Pipelines 210\u003c\/p\u003e \u003cp\u003eData Ingestion 211\u003c\/p\u003e \u003cp\u003eData Preparation 212\u003c\/p\u003e \u003cp\u003eData Segregation 215\u003c\/p\u003e \u003cp\u003eModel Training 217\u003c\/p\u003e \u003cp\u003eModel Evaluation 218\u003c\/p\u003e \u003cp\u003eModel Deployment 220\u003c\/p\u003e \u003cp\u003eModel Monitoring 221\u003c\/p\u003e \u003cp\u003eGCP Options for Deploying Machine Learning Pipeline 221\u003c\/p\u003e \u003cp\u003eCloud AutoML 221\u003c\/p\u003e \u003cp\u003eBigQuery ML 223\u003c\/p\u003e \u003cp\u003eKubeflow 223\u003c\/p\u003e \u003cp\u003eSpark Machine Learning 224\u003c\/p\u003e \u003cp\u003eExam Essentials 225\u003c\/p\u003e \u003cp\u003eReview Questions 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Choosing Training and Serving Infrastructure 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHardware Accelerators 232\u003c\/p\u003e \u003cp\u003eGraphics Processing Units 232\u003c\/p\u003e \u003cp\u003eTensor Processing Units 233\u003c\/p\u003e \u003cp\u003eChoosing Between CPUs, GPUs, and TPUs 233\u003c\/p\u003e \u003cp\u003eDistributed and Single Machine Infrastructure 234\u003c\/p\u003e \u003cp\u003eSingle Machine Model Training 234\u003c\/p\u003e \u003cp\u003eDistributed Model Training 235\u003c\/p\u003e \u003cp\u003eServing Models 236\u003c\/p\u003e \u003cp\u003eEdge Computing with GCP 237\u003c\/p\u003e \u003cp\u003eEdge Computing Overview 237\u003c\/p\u003e \u003cp\u003eEdge Computing Components and Processes 239\u003c\/p\u003e \u003cp\u003eEdge TPU 240\u003c\/p\u003e \u003cp\u003eCloud IoT 240\u003c\/p\u003e \u003cp\u003eExam Essentials 241\u003c\/p\u003e \u003cp\u003eReview Questions 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Measuring, Monitoring, and Troubleshooting Machine Learning Models 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThree Types of Machine Learning Algorithms 248\u003c\/p\u003e \u003cp\u003eSupervised Learning 248\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 253\u003c\/p\u003e \u003cp\u003eAnomaly Detection 254\u003c\/p\u003e \u003cp\u003eReinforcement Learning 254\u003c\/p\u003e \u003cp\u003eDeep Learning 255\u003c\/p\u003e \u003cp\u003eEngineering Machine Learning Models 257\u003c\/p\u003e \u003cp\u003eModel Training and Evaluation 257\u003c\/p\u003e \u003cp\u003eOperationalizing ML Models 262\u003c\/p\u003e \u003cp\u003eCommon Sources of Error in Machine Learning Models 263\u003c\/p\u003e \u003cp\u003eData Quality 264\u003c\/p\u003e \u003cp\u003eUnbalanced Training Sets 264\u003c\/p\u003e \u003cp\u003eTypes of Bias 264\u003c\/p\u003e \u003cp\u003eExam Essentials 265\u003c\/p\u003e \u003cp\u003eReview Questions 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Leveraging Prebuilt Models as a Service 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSight 270\u003c\/p\u003e \u003cp\u003eVision AI 270\u003c\/p\u003e \u003cp\u003eVideo AI 272\u003c\/p\u003e \u003cp\u003eConversation 274\u003c\/p\u003e \u003cp\u003eDialogflow 274\u003c\/p\u003e \u003cp\u003eCloud Text-to-Speech API 275\u003c\/p\u003e \u003cp\u003eCloud Speech-to-Text API 275\u003c\/p\u003e \u003cp\u003eLanguage 276\u003c\/p\u003e \u003cp\u003eTranslation 276\u003c\/p\u003e \u003cp\u003eNatural Language 277\u003c\/p\u003e \u003cp\u003eStructured Data 278\u003c\/p\u003e \u003cp\u003eRecommendations AI API 278\u003c\/p\u003e \u003cp\u003eCloud Inference API 280\u003c\/p\u003e \u003cp\u003eExam Essentials 280\u003c\/p\u003e \u003cp\u003eReview Questions 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix Answers to Review Questions 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1: Selecting Appropriate Storage Technologies 286\u003c\/p\u003e \u003cp\u003eChapter 2: Building and Operationalizing Storage Systems 288\u003c\/p\u003e \u003cp\u003eChapter 3: Designing Data Pipelines 290\u003c\/p\u003e \u003cp\u003eChapter 4: Designing a Data Processing Solution 291\u003c\/p\u003e \u003cp\u003eChapter 5: Building and Operationalizing Processing Infrastructure 293\u003c\/p\u003e \u003cp\u003eChapter 6: Designing for Security and Compliance 295\u003c\/p\u003e \u003cp\u003eChapter 7: Designing Databases for Reliability, Scalability, and Availability 296\u003c\/p\u003e \u003cp\u003eChapter 8: Understanding Data Operations for Flexibility and Portability 298\u003c\/p\u003e \u003cp\u003eChapter 9: Deploying Machine Learning Pipelines 299\u003c\/p\u003e \u003cp\u003eChapter 10: Choosing Training and Serving Infrastructure 301\u003c\/p\u003e \u003cp\u003eChapter 11: Measuring, Monitoring, and Troubleshooting Machine Learning Models 303\u003c\/p\u003e \u003cp\u003eChapter 12: Leveraging Prebuilt Models as a Service 304\u003c\/p\u003e \u003cp\u003eIndex 307\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Medical study \u0026amp; revision guides \u0026amp; reference material [\u003ca title=\"See our other books on Medical study \u0026amp; revision guides \u0026amp; reference material\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Medical%20study%20\u0026amp;%20revision%20guides%20\u0026amp;%20reference%20material%20%5BMR%5D%22\"\u003eMR\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Sybex","offers":[{"title":"Brand New","offer_id":52460633063704,"sku":"9781119618430","price":34.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119618430.jpg?v=1785456305","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/official-google-cloud-certified-professional-data-engineer-study-guide-paperback-softback-9781119618430","provider":"Freshly Printed Books","version":"1.0","type":"link"}